# Configuration options for analysis
# Set to TRUE to load existing results, FALSE to recompute
LOAD_EXISTING_RESULTS <- TRUE
# Analysis parameters
top_n_taxa <- 10 # Number of top taxa to display in partial correlation summaries
# Directory to save/load results
RESULTS_DIR <- "/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results"
# Create results directory if it doesn't exist
if (!dir.exists(RESULTS_DIR)) {
dir.create(RESULTS_DIR, recursive = TRUE)
cat("Created results directory:", RESULTS_DIR, "\n")
}
# Function to save results with timestamp
save_results <- function(results, filename, results_dir = RESULTS_DIR) {
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
full_filename <- file.path(results_dir, paste0(filename, "_", timestamp, ".rds"))
saveRDS(results, full_filename)
cat("Saved results to:", full_filename, "\n")
return(full_filename)
}
# Function to load most recent results file
load_latest_results <- function(filename_pattern, results_dir = RESULTS_DIR) {
files <- list.files(results_dir, pattern = filename_pattern, full.names = TRUE)
if (length(files) == 0) {
cat("No existing results found for pattern:", filename_pattern, "\n")
return(NULL)
}
latest_file <- files[order(file.info(files)$mtime, decreasing = TRUE)[1]]
cat("Loading results from:", latest_file, "\n")
return(readRDS(latest_file))
}
# Display configuration
cat("=== ANALYSIS CONFIGURATION ===\n")
## === ANALYSIS CONFIGURATION ===
cat("Load existing results:", LOAD_EXISTING_RESULTS, "\n")
## Load existing results: TRUE
cat("Results directory:", RESULTS_DIR, "\n")
## Results directory: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results
cat("================================\n\n")
## ================================
This script performs integrated analysis of differentially abundant taxa (DAT) and differentially expressed genes (DEG) across three research questions:
Click on tabs to display additional information.
# Load required libraries for statistical analysis and table creation
library(gt)
library(pheatmap)
library(ggrepel)
library(igraph)
library(tidygraph)
library(ggraph)
library(DESeq2)
library(phyloseq)
library(microViz)
library(vegan)
library(RColorBrewer)
library(clusterProfiler)
library(ggpubr)
library(org.Dr.eg.db)
library(ppcor)
library(GO.db)
library(AnnotationDbi)
library(KEGGREST)
library(nptest)
library(parallel)
library(tidyverse)
# Define treatment order and color palette
treatment_order <- c(
"A- T- P-", # Control
"A- T- P+", # Parasite
"A+ T- P-", # Antibiotics
"A+ T- P+", # Antibiotics_Parasite
"A- T+ P-", # Temperature
"A- T+ P+", # Temperature_Parasite
"A+ T+ P-", # Antibiotics_Temperature
"A+ T+ P+" # Antibiotics_Temperature_Parasite
)
# Custom color palette matching treatment order
treatment_colors <- c(
"#1B9E77", # A- T- P- (Control)
"#D95F02", # A- T- P+ (Parasite)
"#7570B3", # A+ T- P- (Antibiotics)
"#E7298A", # A+ T- P+ (Antibiotics_Parasite)
"#66A61E", # A- T+ P- (Temperature)
"#E6AB02", # A- T+ P+ (Temperature_Parasite)
"#A6761D", # A+ T+ P- (Antibiotics_Temperature)
"#666666" # A+ T+ P+ (Antibiotics_Temperature_Parasite)
)
# Create named vector for color scale
treatment_color_scale <- setNames(treatment_colors, treatment_order)
# Function to extract sample data as dataframe from phyloseq object
samdatAsDataframe <- function(ps) {
samdat <- phyloseq::sample_data(ps)
df <- data.frame(samdat, check.names = FALSE, stringsAsFactors = FALSE)
return(df)
}
# Function to rename variables in phyloseq object
ps_rename <- function(ps, ...) {
ps <- microViz::ps_get(ps)
df <- samdatAsDataframe(ps)
df <- dplyr::rename(.data = df, ...)
phyloseq::sample_data(ps) <- df
return(ps)
}
# SourceFolder function
source(here::here("Code", "R", "Functions", "StartFunctions", "sourceFolder.R"))
# Import all helper functions found in `/Functions`
sourceFolder(here::here("Code", "R", "Functions", "StartFunctions"), T)
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## ========================================
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## Github page: https://github.com/jokergoo/ComplexHeatmap
## Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
##
## If you use it in published research, please cite either one:
## - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
## - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
## genomic data. Bioinformatics 2016.
##
##
## The new InteractiveComplexHeatmap package can directly export static
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## ========================================
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sourceFolder(here::here("Code", "R", "Functions", "HelperFunctions"), T)
## 9 files sourced from folder "/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/R/Functions/HelperFunctions"
# Function to map gene IDs to Entrez IDs
get_entrez <- function(deg_df) {
# This function maps gene symbols (from the input data frame) to Entrez IDs using org.Dr.eg.db.
# INPUT: deg_df - a data frame with a column 'gene_id' (gene symbols)
# OUTPUT: the input data frame with an added 'entrezgene_id' column, filtered to only those with valid Entrez IDs
# Extract gene symbols from the input data frame
gene_ids <- deg_df$gene_id
# Map gene symbols to Entrez IDs using AnnotationDbi
entrez_map <- AnnotationDbi::mapIds(
org.Dr.eg.db,
keys = gene_ids,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
) %>%
tibble::enframe(name = "gene_id", value = "entrezgene_id") %>%
dplyr::filter(!is.na(entrezgene_id))
# Join the Entrez IDs back to the original data, keep only those with valid Entrez IDs
result <- deg_df %>%
dplyr::left_join(entrez_map, by = "gene_id") %>%
dplyr::filter(!is.na(entrezgene_id)) %>%
dplyr::distinct(entrezgene_id, .keep_all = TRUE)
return(result)
}
# Function to perform GO enrichment and summarize correlations for each GO term
get_go_correlations <- function(entrez_df) {
# This function performs GO enrichment analysis on a set of Entrez IDs and summarizes correlation statistics for each GO term.
# INPUT: entrez_df - a data frame with 'entrezgene_id', 'correlation', 'TaxaID', etc.
# OUTPUT: a summary data frame with GO terms, p-values, mean correlations, and gene/taxa lists
cat("\nProcessing contrast with", nrow(entrez_df), "genes\n")
# Run GO enrichment analysis (Biological Process ontology)
go_results <- enrichGO(
gene = entrez_df$entrezgene_id,
OrgDb = org.Dr.eg.db,
keyType = "ENTREZID",
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.1,
qvalueCutoff = 0.2,
readable = TRUE
) %>%
as.data.frame() %>%
dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
dplyr::mutate(log_padj = -log10(p.adjust))
# Get GO term categories and definitions for each GO term
go_categories <- AnnotationDbi::select(
GO.db,
keys = go_results$ID,
columns = c("GOID", "TERM", "ONTOLOGY", "DEFINITION"),
keytype = "GOID"
)
# For each GO term, get its parent terms (hierarchy)
go_parents <- lapply(go_results$ID, function(go_id) {
parents <- GO.db::GOBPANCESTOR[[go_id]]
if (is.null(parents)) return(NA)
parent_terms <- AnnotationDbi::select(
GO.db,
keys = parents,
columns = c("GOID", "TERM"),
keytype = "GOID"
)
return(list(parent_terms$TERM))
})
# Create a mapping of GO IDs to their parent terms
go_hierarchy <- tibble::tibble(
GOID = go_results$ID,
parent_terms = go_parents
)
# Map Entrez IDs to gene symbols for easier interpretation
symbol_to_entrez <- AnnotationDbi::mapIds(
org.Dr.eg.db,
keys = entrez_df$entrezgene_id,
column = "SYMBOL",
keytype = "ENTREZID",
multiVals = "first"
) %>%
tibble::enframe(name = "entrezgene_id", value = "SYMBOL") %>%
dplyr::filter(!is.na(SYMBOL))
# Prepare a mapping of gene IDs to correlation and taxa information
gene_cor_map <- entrez_df %>%
dplyr::select(entrezgene_id, correlation, TaxaID, abs_correlation) %>%
dplyr::left_join(symbol_to_entrez, by = "entrezgene_id") %>%
dplyr::select(SYMBOL, correlation, TaxaID, abs_correlation) %>%
dplyr::filter(!is.na(SYMBOL))
# Expand the geneID column (GO term members) and join with correlation/taxa info
go_results_long <- go_results %>%
tidyr::separate_rows(geneID, sep = "/") %>%
dplyr::left_join(gene_cor_map, by = c("geneID" = "SYMBOL")) %>%
dplyr::left_join(go_categories, by = c("ID" = "GOID")) %>%
dplyr::left_join(go_hierarchy, by = c("ID" = "GOID"))
# Summarize by GO term: mean correlation, number of positive/negative, gene/taxa lists, etc.
go_results_summary <- go_results_long %>%
dplyr::group_by(ID, Description, TERM, ONTOLOGY, DEFINITION, parent_terms, p.adjust, Count, log_padj) %>%
dplyr::summarise(
mean_correlation = mean(correlation, na.rm = TRUE),
mean_abs_correlation = mean(abs_correlation, na.rm = TRUE),
n_positive = sum(correlation > 0, na.rm = TRUE),
n_negative = sum(correlation < 0, na.rm = TRUE),
gene_list = list(geneID),
correlation_list = list(correlation),
taxa_list = list(TaxaID),
.groups = "drop"
) %>%
dplyr::arrange(p.adjust)
return(go_results_summary)
}
# Function to enrich KEGG pathways for a set of significant gene-taxa correlations
get_kegg_pathways <- function(sig_partial_cor) {
# This function performs KEGG pathway enrichment for a set of significant gene-taxa correlations.
# INPUT: sig_partial_cor - data frame with at least 'gene_id' (gene symbol)
# OUTPUT: summary table of enriched KEGG pathways, with gene/taxa/correlation info for each pathway
cat("\nProcessing KEGG analysis for significant correlations\n")
# Get unique gene symbols from significant correlations
sig_genes <- unique(sig_partial_cor$gene_id)
cat("Number of significant genes:", length(sig_genes), "\n")
# Map gene symbols to Entrez IDs
entrez_ids <- AnnotationDbi::mapIds(
org.Dr.eg.db,
keys = sig_genes,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
) %>%
tibble::enframe(name = "SYMBOL", value = "ENTREZID") %>%
dplyr::filter(!is.na(ENTREZID))
cat("Number of genes successfully mapped to Entrez IDs:", nrow(entrez_ids), "\n")
# Run KEGG enrichment analysis
kegg_results <- enrichKEGG(
gene = entrez_ids$ENTREZID,
organism = 'dre', # Danio rerio
keyType = 'ncbi-geneid',
pAdjustMethod = "BH",
pvalueCutoff = .1,
qvalueCutoff = .2
)
if (is.null(kegg_results)) {
cat("No KEGG pathways found.\n")
return(NULL)
}
kegg_results <- as.data.frame(kegg_results) %>%
dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
dplyr::mutate(log_padj = -log10(p.adjust))
# For each pathway, get additional info (name, description, class)
kegg_info <- lapply(kegg_results$ID, function(path_id) {
clean_id <- gsub("dre", "", path_id)
tryCatch({
path_info <- KEGGREST::keggGet(clean_id)
if (length(path_info) > 0) {
return(list(
name = path_info[[1]]$NAME,
description = ifelse(is.null(path_info[[1]]$DESCRIPTION),
path_info[[1]]$NAME,
path_info[[1]]$DESCRIPTION),
class = ifelse(is.null(path_info[[1]]$CLASS),
"Not specified",
path_info[[1]]$CLASS)
))
}
}, error = function(e) {
tryCatch({
path_info <- KEGGREST::keggGet(path_id)
if (length(path_info) > 0) {
return(list(
name = path_info[[1]]$NAME,
description = ifelse(is.null(path_info[[1]]$DESCRIPTION),
path_info[[1]]$NAME,
path_info[[1]]$DESCRIPTION),
class = ifelse(is.null(path_info[[1]]$CLASS),
"Not specified",
path_info[[1]]$CLASS)
))
}
}, error = function(e2) {
return(list(
name = "Not available",
description = "Not available",
class = "Not available"
))
})
})
})
# Create a mapping of KEGG IDs to their information
kegg_details <- tibble::tibble(
KEGGID = kegg_results$ID,
Name = sapply(kegg_info, function(x) x$name),
Description = sapply(kegg_info, function(x) x$description),
Class = sapply(kegg_info, function(x) x$class)
)
# Map Entrez IDs to gene symbols
id_mapping <- entrez_ids %>%
dplyr::select(ENTREZID, SYMBOL)
# Expand geneID column and join with gene symbol mapping
kegg_results_long <- kegg_results %>%
tidyr::separate_rows(geneID, sep = "/") %>%
dplyr::mutate(geneID = as.character(geneID)) %>%
dplyr::left_join(id_mapping, by = c("geneID" = "ENTREZID"))
# For each pathway, summarize gene-taxa-correlation relationships
kegg_summary <- kegg_results_long %>%
dplyr::group_by(ID) %>%
dplyr::summarise(
Name = Description[1],
Description = kegg_details$Description[kegg_details$KEGGID == ID[1]][1],
Class = kegg_details$Class[kegg_details$KEGGID == ID[1]][1],
p.adjust = p.adjust[1],
Count = Count[1],
Gene_List = list(unique(SYMBOL)),
.groups = "drop"
) %>%
dplyr::mutate(
Gene_Taxa_Relations = lapply(Gene_List, function(genes) {
sig_partial_cor %>%
dplyr::filter(gene_id %in% genes) %>%
dplyr::select(gene_id, TaxaID, correlation) %>%
dplyr::arrange(gene_id, TaxaID)
}),
Taxa_List = lapply(Gene_Taxa_Relations, function(rel) {
unique(rel$TaxaID)
}),
Correlation_List = lapply(Gene_Taxa_Relations, function(rel) {
rel$correlation
})
) %>%
dplyr::arrange(p.adjust)
return(kegg_summary)
}
# Helper function to get top correlations by correlation, taxa, or gene
gen_get_top_correlations <- function(correlation_results, top_n = 100, top_by = c("correlation", "taxa", "gene")) {
top_by <- match.arg(top_by)
# Check if we have any significant correlations
sig_correlations <- correlation_results %>%
dplyr::filter(fdr < 0.05)
if(nrow(sig_correlations) == 0) {
cat("WARNING: No significant correlations found (FDR < 0.05). Returning empty data frame.\n")
return(data.frame())
}
if (top_by == "correlation") {
top_correlations <- sig_correlations %>%
dplyr::arrange(desc(abs_correlation)) %>%
head(top_n)
} else if (top_by == "taxa") {
top_taxa <- sig_correlations %>%
dplyr::group_by(TaxaID) %>%
dplyr::summarise(n = n()) %>%
dplyr::arrange(desc(n)) %>%
head(top_n) %>%
dplyr::pull(TaxaID)
if(length(top_taxa) == 0) {
cat("WARNING: No taxa found with significant correlations. Returning empty data frame.\n")
return(data.frame())
}
top_correlations <- sig_correlations %>%
dplyr::filter(TaxaID %in% top_taxa)
} else if (top_by == "gene") {
top_genes <- sig_correlations %>%
dplyr::group_by(gene_id) %>%
dplyr::summarise(n = n()) %>%
dplyr::arrange(desc(n)) %>%
head(top_n) %>%
dplyr::pull(gene_id)
if(length(top_genes) == 0) {
cat("WARNING: No genes found with significant correlations. Returning empty data frame.\n")
return(data.frame())
}
top_correlations <- sig_correlations %>%
dplyr::filter(gene_id %in% top_genes)
}
return(top_correlations)
}
# Function to perform integrated analysis for a single research question
perform_integrated_analysis <- function(question_name,
dat_results,
deg_results,
taxa_counts,
expr_counts,
metadata,
treatment_comparison,
top_n = 100,
top_by = c("correlation", "taxa", "gene")) {
# This function performs the full DEGxDAT analysis for a single research question (single treatment comparison).
# INPUTS:
# - question_name: string, name of the research question
# - dat_results: data frame of DAT results
# - deg_results: data frame of DEG results
# - taxa_counts: OTU/taxa count matrix
# - expr_counts: gene expression count matrix
# - metadata: sample metadata
# - treatment_comparison: vector of treatment names to compare
# - top_n: number of top correlations to use for partial correlation analysis
# - top_by: selection of top correlations by correlation, taxa, or gene
# OUTPUT: list with all results for this comparison (correlations, significant genes/taxa, etc.)
cat("\n=== ANALYSIS FOR:", question_name, "===\n")
cat("Treatment comparison:", paste(treatment_comparison, collapse = " vs "), "\n")
# Set seed for reproducibility
set.seed(42)
# Filter for significant DEGs and DATs
deg_sig <- deg_results %>%
dplyr::filter(padj < 0.05)
dat_sig <- dat_results %>%
dplyr::filter(qval < 0.05)
cat("Significant genes:", nrow(deg_sig), "\n")
cat("Significant taxa:", nrow(dat_sig), "\n")
# Check if we have any significant genes or taxa
if(nrow(deg_sig) == 0 || nrow(dat_sig) == 0) {
cat("WARNING: No significant genes or taxa found. Returning empty results.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = data.frame(),
sig_correlations = data.frame(),
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = character(0)
))
}
# Get relevant samples for this comparison
rna_samples <- metadata %>%
dplyr::filter(Treatment %in% treatment_comparison) %>%
dplyr::filter(Sample %in% colnames(expr_counts)) %>%
dplyr::pull(Sample) %>%
as.character()
cat("Samples for analysis:", length(rna_samples), "\n")
# Check if we have enough samples
if(length(rna_samples) < 3) {
cat("WARNING: Insufficient samples for analysis. Returning empty results.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = data.frame(),
sig_correlations = data.frame(),
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
# Filter expression data for significant genes and relevant samples
filtered_expr <- expr_counts %>%
dplyr::filter(gene %in% deg_sig$gene) %>%
dplyr::select(gene, gene_id, gene_name, any_of(rna_samples))
# Filter taxa data for significant taxa and relevant samples
filtered_taxa <- taxa_counts %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "Sample") %>%
dplyr::mutate(Sample = gsub("^f", "", Sample)) %>%
dplyr::select(Sample, any_of(dat_sig$taxa)) %>%
dplyr::filter(Sample %in% rna_samples)
# Check if we have data after filtering
if(nrow(filtered_expr) == 0 || ncol(filtered_taxa) <= 1) {
cat("WARNING: No expression or taxa data available after filtering. Returning empty results.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = data.frame(),
sig_correlations = data.frame(),
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
# Normalize gene expression data (z-score per gene)
normalized_expr <- filtered_expr %>%
tidyr::pivot_longer(
cols = -c(gene, gene_id, gene_name),
names_to = "Sample",
values_to = "count"
) %>%
dplyr::group_by(gene_id) %>%
dplyr::mutate(
z_score = (count - mean(count)) / sd(count)
) %>%
dplyr::select(-count) %>%
tidyr::pivot_wider(
names_from = Sample,
values_from = z_score
)
# Normalize taxa data (z-score per taxon)
prepared_taxa <- filtered_taxa %>%
tidyr::pivot_longer(
cols = -Sample,
names_to = "TaxaID",
values_to = "abundance"
) %>%
dplyr::group_by(TaxaID) %>%
dplyr::mutate(
z_score = (abundance - mean(abundance)) / sd(abundance)
) %>%
dplyr::select(Sample, TaxaID, z_score) %>%
dplyr::rename(abundance = z_score) %>%
dplyr::mutate(Sample = as.character(Sample))
# Prepare expression data for correlation
prepared_expr <- normalized_expr %>%
tidyr::pivot_longer(
cols = -c(gene, gene_id, gene_name),
names_to = "Sample",
values_to = "z_score"
)
# Calculate gene-taxa correlations (Spearman)
correlation_results <- prepared_expr %>%
dplyr::left_join(
prepared_taxa,
by = "Sample"
) %>%
dplyr::group_by(gene_id, gene_name, TaxaID) %>%
dplyr::summarise(
correlation = cor(z_score, abundance, method = "spearman"),
.groups = "drop"
) %>%
dplyr::mutate(
abs_correlation = abs(correlation)
) %>%
dplyr::arrange(desc(abs_correlation))
# Calculate significance (p-value, FDR)
correlation_results <- correlation_results %>%
dplyr::mutate(
p_value = 2 * (1 - pnorm(abs(correlation) * sqrt((nrow(filtered_taxa) - 2) / (1 - correlation^2)))),
fdr = p.adjust(p_value, method = "BH")
)
# Get significant correlations (FDR < 0.05)
sig_correlations <- correlation_results %>%
dplyr::filter(fdr < 0.05) %>%
dplyr::arrange(desc(abs_correlation))
cat("Significant correlations:", nrow(sig_correlations), "\n")
# Check if we have any significant correlations before proceeding with partial correlation
if(nrow(sig_correlations) == 0) {
cat("WARNING: No significant correlations found. Skipping partial correlation analysis.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = correlation_results,
sig_correlations = sig_correlations,
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
# Get top gene-taxa pairs for partial correlation based on user selection
top_correlations <- gen_get_top_correlations(correlation_results, top_n = top_n, top_by = top_by)
# Check if we have any top correlations
if(nrow(top_correlations) == 0) {
cat("WARNING: No top correlations found for partial correlation analysis.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = correlation_results,
sig_correlations = sig_correlations,
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
cat(paste0("Top ", nrow(top_correlations), " by ", top_by, " for partial correlation:\n"))
print(top_correlations %>% dplyr::select(gene_id, TaxaID, abs_correlation))
# Only run partial correlation on these gene-taxa pairs
top_pairs <- top_correlations %>%
dplyr::select(gene_id, TaxaID) %>%
dplyr::distinct()
# Check if we have valid gene-taxa pairs
if(nrow(top_pairs) == 0 || any(is.na(top_pairs$gene_id)) || any(is.na(top_pairs$TaxaID))) {
cat("WARNING: No valid gene-taxa pairs found for partial correlation analysis.\n")
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = correlation_results,
sig_correlations = sig_correlations,
partial_cor_results = data.frame(),
sig_partial_correlations = data.frame(),
failed_pairs = list(),
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
# Perform partial correlation analysis (controlling for worm counts)
cat("Performing partial correlation analysis...\n")
# Convert filtered expression data to wide format with samples as rows
expr_wide <- filtered_expr %>%
tidyr::pivot_longer(
cols = -c(gene, gene_id, gene_name),
names_to = "Sample",
values_to = "count"
) %>%
dplyr::group_by(gene_id) %>%
dplyr::mutate(
z_score = (count - mean(count)) / sd(count)
) %>%
dplyr::select(-c(count, gene_name, gene)) %>%
tidyr::pivot_wider(
names_from = gene_id,
values_from = z_score
) %>%
dplyr::arrange(as.numeric(Sample))
# Convert filtered taxa data to wide format with samples as rows
taxa_wide <- filtered_taxa %>%
tidyr::pivot_longer(
cols = -Sample,
names_to = "TaxaID",
values_to = "abundance"
) %>%
tidyr::pivot_wider(
names_from = TaxaID,
values_from = abundance
) %>%
dplyr::mutate(Sample = as.character(Sample)) %>%
dplyr::arrange(as.numeric(Sample))
# Create control variable matrix (worm counts)
control_matrix <- metadata %>%
dplyr::filter(Sample %in% rna_samples) %>%
dplyr::arrange(Sample) %>%
dplyr::select(Total.Worm.Count) %>%
as.matrix()
# Verify sample alignment
if(!all(expr_wide$Sample == taxa_wide$Sample) ||
!all(expr_wide$Sample == rownames(control_matrix))) {
stop("Sample order mismatch between expression, taxa, and control data")
}
# Initialize results storage for partial correlations
partial_cor_results <- list()
failed_pairs <- list()
# Set up parallel processing
library(parallel)
cl <- makeCluster(6) # Create a cluster with 6 cores
on.exit(stopCluster(cl)) # Ensure cluster is stopped when done
# Calculate partial correlations for the top pairs
for(i in 1:nrow(top_pairs)) {
gene <- top_pairs$gene_id[i]
taxa <- top_pairs$TaxaID[i]
# Skip if gene or taxa is NA
if(is.na(gene) || is.na(taxa)) {
failed_pairs[[paste(gene, taxa, sep = "_")]] <- "NA gene or taxa ID"
next
}
# Check if gene and taxa exist in the data
if(!gene %in% colnames(expr_wide) || !taxa %in% colnames(taxa_wide)) {
failed_pairs[[paste(gene, taxa, sep = "_")]] <- "Gene or taxa not found in data"
next
}
# Get data for this gene-taxa pair
x <- expr_wide[[gene]]
y <- taxa_wide[[taxa]]
# Skip if any data is missing
if(any(is.na(c(x, y)))) {
failed_pairs[[paste(gene, taxa, sep = "_")]] <- "Missing data"
next
}
set.seed(42)
# Calculate partial correlation
res_pcor <- try(nptest::np.cor.test(
x = x,
y = y,
z = control_matrix,
partial = TRUE,
parallel = TRUE,
cl = cl,
R = 1000,
na.rm = TRUE
), silent = TRUE)
# Only store results if calculation was successful
if(!inherits(res_pcor, "try-error")) {
partial_cor_results[[paste(gene, taxa, sep = "_")]] <- list(
gene_id = gene,
taxa_id = taxa,
correlation = res_pcor$estimate,
p_value = res_pcor$p.value
)
} else {
failed_pairs[[paste(gene, taxa, sep = "_")]] <- as.character(res_pcor)
}
}
# Convert partial correlation results to data frame
if(length(partial_cor_results) > 0) {
partial_cor_df <- do.call(rbind, lapply(partial_cor_results, function(x) {
data.frame(
gene_id = x$gene_id,
taxa_id = x$taxa_id,
correlation = x$correlation,
p_value = x$p_value
)
})) %>%
dplyr::mutate(
fdr = p.adjust(p_value, method = "BH"),
abs_correlation = abs(correlation)
) %>%
dplyr::arrange(desc(abs_correlation))
# Get significant partial correlations (FDR < 0.1)
sig_partial_cor <- partial_cor_df %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::arrange(desc(abs_correlation))
cat("Significant partial correlations (FDR < 0.1):", nrow(sig_partial_cor), "\n")
} else {
partial_cor_df <- data.frame()
sig_partial_cor <- data.frame()
cat("No successful partial correlations were calculated.\n")
}
# Return all results for this comparison
return(list(
question_name = question_name,
treatment_comparison = treatment_comparison,
correlation_results = correlation_results,
sig_correlations = sig_correlations,
partial_cor_results = partial_cor_df,
sig_partial_correlations = sig_partial_cor,
failed_pairs = failed_pairs,
deg_sig = deg_sig,
dat_sig = dat_sig,
samples = rna_samples
))
}
# Function to perform all pairwise comparisons for a research question
perform_pairwise_analysis <- function(question_name,
dat_results,
deg_results,
taxa_counts,
expr_counts,
metadata,
base_treatment,
comparison_treatments,
top_n = 100,
top_by = c("correlation", "taxa", "gene")) {
# This function runs DEGxDAT analysis for all pairwise comparisons between a base treatment and a set of comparison treatments.
# INPUTS:
# - question_name: string, name of the research question
# - dat_results, deg_results, taxa_counts, expr_counts, metadata: as above
# - base_treatment: string, the reference treatment
# - comparison_treatments: vector of treatments to compare to base
# OUTPUT: list with all pairwise results and combined summary tables
cat("\n=== PAIRWISE ANALYSIS FOR:", question_name, "===\n")
cat("Base treatment:", base_treatment, "\n")
cat("Comparison treatments:", paste(comparison_treatments, collapse = ", "), "\n")
# Store results for each pairwise comparison
pairwise_results <- list()
# Loop over each comparison treatment
for(i in seq_along(comparison_treatments)) {
comparison_treatment <- comparison_treatments[i]
comparison_name <- paste(base_treatment, "vs", comparison_treatment)
cat("\n--- Comparison", i, ":", comparison_name, "---\n")
# Create treatment comparison vector
treatment_comparison <- c(base_treatment, comparison_treatment)
# Run integrated analysis for this pair
result <- perform_integrated_analysis(
question_name = comparison_name,
dat_results = dat_results,
deg_results = deg_results,
taxa_counts = taxa_counts,
expr_counts = expr_counts,
metadata = metadata,
treatment_comparison = treatment_comparison,
top_n = top_n,
top_by = top_by
)
# Add comparison info to result
result$comparison_name <- comparison_name
result$base_treatment <- base_treatment
result$comparison_treatment <- comparison_treatment
pairwise_results[[comparison_name]] <- result
}
# Combine significant correlations from all pairs (handle empty data frames)
combined_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$sig_correlations) > 0) {
x$sig_correlations %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
# Combine all correlations from all pairs (handle empty data frames)
combined_all_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$correlation_results) > 0) {
x$correlation_results %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
# Combine partial correlation results from all pairs
combined_partial_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$partial_cor_results) > 0) {
x$partial_cor_results %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
# Combine significant partial correlations from all pairs
combined_sig_partial_correlations <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$sig_partial_correlations) > 0) {
x$sig_partial_correlations %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
# Combine significant DEGs and DATs from all pairs (handle empty data frames)
combined_deg <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$deg_sig) > 0) {
x$deg_sig %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
combined_dat <- do.call(rbind, lapply(pairwise_results, function(x) {
if(nrow(x$dat_sig) > 0) {
x$dat_sig %>%
dplyr::mutate(
comparison_name = x$comparison_name,
base_treatment = x$base_treatment,
comparison_treatment = x$comparison_treatment
)
} else {
data.frame()
}
}))
# Get all unique samples used in any pairwise comparison
all_samples <- unique(unlist(lapply(pairwise_results, function(x) x$samples)))
# Print summary of results
cat("\n=== PAIRWISE ANALYSIS SUMMARY ===\n")
cat("Total comparisons completed:", length(pairwise_results), "\n")
cat("Comparisons with significant correlations:", sum(sapply(pairwise_results, function(x) nrow(x$sig_correlations) > 0)), "\n")
cat("Comparisons with significant partial correlations:", sum(sapply(pairwise_results, function(x) nrow(x$sig_partial_correlations) > 0)), "\n")
cat("Total significant correlations:", nrow(combined_correlations), "\n")
cat("Total significant partial correlations:", nrow(combined_sig_partial_correlations), "\n")
return(list(
question_name = question_name,
base_treatment = base_treatment,
comparison_treatments = comparison_treatments,
pairwise_results = pairwise_results,
combined_correlations = combined_correlations,
combined_all_correlations = combined_all_correlations,
combined_partial_correlations = combined_partial_correlations,
combined_sig_partial_correlations = combined_sig_partial_correlations,
combined_deg = combined_deg,
combined_dat = combined_dat,
all_samples = all_samples
))
}
ps.tmp <- readRDS("/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Data/Robjects/pseq_uncleaned_05052025.rds")
ps.cleaned <-
ps.tmp %>%
## Update Metadata
ps_rename(Time = Timepoint) %>%
microViz::ps_mutate(
Treatment = case_when(
Antibiotics == 0 & Temperature == 0 & Pathogen == 0 ~ "A- T- P-",
Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "A- T- P+",
Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "A+ T- P-",
Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "A+ T- P+",
Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "A- T+ P-",
Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "A- T+ P+",
Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "A+ T+ P-",
Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "A+ T+ P+",
TRUE ~ "Unknown"
), .after = "Pathogen"
) %>%
microViz::ps_mutate(Sample = fecal.sample.number, .before = 1) %>%
microViz::ps_mutate(Sample = gsub("^f", "", Sample)) %>%
microViz::ps_filter(Treatment != "Unknown") %>%
microViz::ps_mutate(
History = case_when(
Antibiotics + Temperature == 0 ~ 0,
Antibiotics + Temperature == 1 ~ 1,
Antibiotics + Temperature == 2 ~ 2,
), .after = "Treatment"
) %>%
## Additional metadata updates, factorizing metadata
microViz::ps_mutate(
# Create treatment code
treatment_code = case_when(
Antibiotics == 0 & Temperature == 0 & Pathogen == 0 ~ "Aneg_Tneg_Pneg",
Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "Aneg_Tneg_Ppos",
Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "Apos_Tneg_Pneg",
Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "Apos_Tneg_Ppos",
Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "Aneg_Tpos_Pneg",
Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "Aneg_Tpos_Ppos",
Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "Apos_Tpos_Pneg",
Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "Apos_Tpos_Ppos"
),
# Create treatment group factor
treatment_group = case_when(
Antibiotics == 0 & Temperature == 0 & Pathogen == 1 ~ "Parasite",
Antibiotics == 1 & Temperature == 0 & Pathogen == 0 ~ "Antibiotics",
Antibiotics == 1 & Temperature == 0 & Pathogen == 1 ~ "Antibiotics_Parasite",
Antibiotics == 0 & Temperature == 1 & Pathogen == 0 ~ "Temperature",
Antibiotics == 0 & Temperature == 1 & Pathogen == 1 ~ "Temperature_Parasite",
Antibiotics == 1 & Temperature == 1 & Pathogen == 0 ~ "Antibiotics_Temperature",
Antibiotics == 1 & Temperature == 1 & Pathogen == 1 ~ "Antibiotics_Temperature_Parasite",
TRUE ~ "Control"
),
# Convert to factor with appropriate levels
treatment_group = factor(treatment_group,
levels = c("Control", "Parasite",
"Antibiotics", "Antibiotics_Parasite",
"Temperature", "Temperature_Parasite",
"Antibiotics_Temperature", "Antibiotics_Temperature_Parasite")
),
treatment_code = factor(treatment_code, levels = treatment_order),
# Create time point factor
time_point = factor(Time, levels = c(0, 14, 18, 25, 29, 60)),
# Create pathogen status factor
pathogen_status = factor(ifelse(Pathogen == 1, "Exposed", "Unexposed"),
levels = c("Unexposed", "Exposed")),
# Create sex factor
sex = factor(Sex, levels = c("M", "F"))
) %>%
microViz::ps_mutate(Treatment = factor(Treatment, levels = treatment_order)) %>%
microViz::ps_mutate(Exp_Type = case_when(
Treatment %in% c("A- T- P-", "A- T- P+") ~ "No prior stressor(s)",
Treatment %in% c("A+ T- P-", "A+ T- P+") ~ "Antibiotics",
Treatment %in% c("A- T+ P-", "A- T+ P+") ~ "Temperature",
Treatment %in% c("A+ T+ P-", "A+ T+ P+") ~ "Combined",
)) %>%
microViz::ps_mutate(Exp_Type = factor(Exp_Type, levels = c("No prior stressor(s)", "Antibiotics", "Temperature", "Combined"))) %>%
# Fix names for taxonomic ranks not identified
microViz::tax_fix(suffix_rank = "current", anon_unique = T, unknown = NA) %>%
# Filter for any samples that contain more than 5000 reads
microViz::ps_filter(sample_sums(.) > 5000) %>%
# Any taxa not found in at least 3 samples are removed
microViz::tax_filter(min_prevalence = 3, undetected = 0) %>%
# Remove any unwanted reads
microViz::tax_select(c("Mitochondria", "Chloroplast", "Eukaryota"), deselect = TRUE) %>%
microViz::tax_select(c("Bacteria, Phylum"), deselect = TRUE)
# Read and process OTU table for all treatments
taxa_counts.all <- ps.cleaned %>%
microViz::tax_agg(rank = "Genus") %>%
microViz::otu_get()
# Create taxa counts for different treatment comparisons
taxa_counts.parasite <- ps.cleaned %>%
microViz::ps_filter(Treatment %in% c("A- T- P-", "A- T- P+")) %>%
microViz::tax_agg(rank = "Genus") %>%
microViz::otu_get()
taxa_counts.priorStressors <- ps.cleaned %>%
microViz::ps_filter(Treatment %in% c("A- T- P+", "A+ T- P+", "A- T+ P+", "A+ T+ P+")) %>%
microViz::tax_agg(rank = "Genus") %>%
microViz::otu_get()
taxa_counts.noPriorStressors <- ps.cleaned %>%
microViz::ps_filter(Treatment %in% c("A- T- P-", "A+ T- P-", "A- T+ P-", "A+ T+ P-")) %>%
microViz::tax_agg(rank = "Genus") %>%
microViz::otu_get()
# Question 0: All
dat_results.all <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/All_60DPE__TREATMENT__significant_results.tsv') %>%
dplyr::rename(taxa = feature)
## Rows: 610 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 1: Parasite Exposure Response
dat_results.parasite <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/ParasiteExp_60DPE__TREATMENT__significant_results.tsv') %>%
dplyr::rename(taxa = feature)
## Rows: 76 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 2: Historical Contingency of Parasite Response
dat_results.historical_contingency <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/PriorStressParaExp_60DPE__TREATMENT__significant_results.tsv') %>%
dplyr::rename(taxa = feature)
## Rows: 97 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 3: Historical Contingency of Recovery
dat_results.recovery <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffAbund/Results/PriorStressNoParaExp_60DPE__TREATMENT__significant_results.tsv') %>%
dplyr::rename(taxa = feature)
## Rows: 196 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): feature, metadata, value
## dbl (6): coef, stderr, N, N.not.0, pval, qval
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 0: All
deg_results.all <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/All_Treatments__significant_results.tsv')
## Rows: 21638 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 1: Parasite Exposure Response
deg_results.parasite <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Parasite_Effect__significant_results.tsv')
## Rows: 3753 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 2: Historical Contingency of Parasite Response
deg_results.historical_contingency <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Historical_Contingency__significant_results.tsv')
## Rows: 204 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Question 3: Historical Contingency of Recovery
deg_results.recovery <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/Recovery_Analysis__significant_results.tsv')
## Rows: 199 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (3): gene, metadata, value
## dbl (5): log2FoldChange, padj, baseMean, pvalue, stat
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Import raw expression data
expr_counts <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Data/Transcriptomics/Results/rnaseq/051525/salmon/salmon.merged.gene_counts_length_scale__Corrected_f136-f138.tsv') %>%
# Clean up column names
dplyr::rename_with(~gsub("TS047_RoL_RNA_", "", .), -c(gene_id, gene_name)) %>%
dplyr::mutate(gene = gene_id, .before = 1) #%>%
## Rows: 39447 Columns: 74
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): gene_id, gene_name
## dbl (72): TS047_RoL_RNA_110, TS047_RoL_RNA_112, TS047_RoL_RNA_114, TS047_RoL...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# dplyr::rename(`136` = `138`)
# Get samples from raw expression data
expr_samples <- colnames(expr_counts)[-c(1:3)] # Exclude gene_id and gene_name columns
# Import metadata to get treatment info
metadata <- read_tsv('/Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DiffExpGene/Results/sample_metadata.tsv') %>%
dplyr::mutate(Treatment_long = Treatment) %>%
dplyr::rename(Sample = sample) %>%
dplyr::mutate(
Treatment = stringr::str_c(
dplyr::case_when(Antibiotics == 0 ~ "A-", Antibiotics == 1 ~ "A+"),
dplyr::case_when(Temperature == 0 ~ " T-", Temperature == 1 ~ " T+"),
dplyr::case_when(Pathogen == 0 ~ " P-", Pathogen == 1 ~ " P+")
)
) %>%
dplyr::relocate(Treatment, .after = Pathogen) %>%
dplyr::arrange(desc(Treatment))
## Rows: 71 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (2): Treatment, Treatment_Plot
## dbl (10): sample, Sample_Name, Time, Antibiotics, Temperature, Pathogen, His...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Print summary of available data
cat("\n=== DATA SUMMARY ===\n")
##
## === DATA SUMMARY ===
# Create summary table
data_summary <- tibble::tibble(
Metric = c("Total samples in expression data", "Total samples in metadata"),
Count = c(length(expr_samples), nrow(metadata))
)
# Display summary table
data_summary %>%
gt::gt() %>%
gt::tab_header(
title = "Data Summary",
subtitle = "Overview of available samples"
) %>%
gt::cols_label(
Metric = "Data Type",
Count = "Number of Samples"
)
| Data Summary | |
| Overview of available samples | |
| Data Type | Number of Samples |
|---|---|
| Total samples in expression data | 72 |
| Total samples in metadata | 71 |
# Create treatment distribution table
metadata %>%
dplyr::count(Treatment) %>%
dplyr::arrange(desc(n)) %>%
gt::gt() %>%
gt::tab_header(
title = "Treatment Distribution",
subtitle = "Number of samples per treatment group"
) %>%
gt::cols_label(
Treatment = "Treatment Group",
n = "Number of Samples"
)
| Treatment Distribution | |
| Number of samples per treatment group | |
| Treatment Group | Number of Samples |
|---|---|
| A+ T+ P+ | 12 |
| A+ T- P+ | 12 |
| A- T+ P+ | 12 |
| A- T- P+ | 11 |
| A+ T+ P- | 6 |
| A+ T- P- | 6 |
| A- T+ P- | 6 |
| A- T- P- | 6 |
## Attempting to load existing results for Question 0...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_all_20250630_200742.rds
## Successfully loaded existing results for Question 0.
##
## === QUESTION 0 SUMMARY ===
| Question 2: Historical Contingency of Parasite Response | |
| Summary of analysis results | |
| Analysis Metric | Result |
|---|---|
| Question | Historical Contingency of Parasite Response |
| Base Treatment | A- T- P- |
| Comparison Treatments | A- T- P+, A+ T- P-, A+ T- P+, A- T+ P-, A- T+ P+, A+ T+ P-, A+ T+ P+ |
| Total Samples | 72 |
| Number of Pairwise Comparisons | 7 |
| Pairwise Comparison Details | ||||
| Results for each individual comparison | ||||
| Comparison | N Samples | N Significant Genes | N Significant Taxa | N Significant Correlations |
|---|---|---|---|---|
| A- T- P- vs A- T- P+ | 18 | 3753 | 84 | 20499 |
| A- T- P- vs A+ T- P- | 12 | 35 | 75 | 588 |
| A- T- P- vs A+ T- P+ | 18 | 6473 | 93 | 7888 |
| A- T- P- vs A- T+ P- | 12 | 47 | 73 | 3407 |
| A- T- P- vs A- T+ P+ | 18 | 6196 | 89 | 1969 |
| A- T- P- vs A+ T+ P- | 12 | 117 | 77 | 1656 |
| A- T- P- vs A+ T+ P+ | 18 | 5017 | 119 | 4563 |
##
## Top 10 strongest correlations across all pairwise comparisons:
| gene_id | gene_name | TaxaID | correlation | abs_correlation | p_value | fdr | comparison_name | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|---|
| LOC103909395 | LOC103909395 | Vogesella | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC141376179 | LOC141376179 | Chitinimonas | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC141376179 | LOC141376179 | Flavihumibacter | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC141376179 | LOC141376179 | Hyphomicrobium | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC141376179 | LOC141376179 | Mesorhizobium | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| zgc:171500 | zgc:171500 | Pseudoxanthomonas | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P+ | A- T- P- | A+ T- P+ |
| zgc:171500 | zgc:171500 | SWB02 | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P+ | A- T- P- | A+ T- P+ |
| zgc:171500 | zgc:171500 | WY65 | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A+ T- P+ | A- T- P- | A+ T- P+ |
| LOC137490512 | LOC137490512 | Mesorhizobium | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC141374967 | LOC141374967 | Afipia | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
## Warning: Removed 831498 rows containing non-finite outside the scale range
## (`stat_bin()`).
##
## Correlation summary by comparison:
| comparison_name | n_correlations | mean_correlation | median_correlation | n_significant |
|---|---|---|---|---|
| A- T- P- vs A+ T+ P+ | 729756 | −0.019 | −0.026 | 4563 |
| A- T- P- vs A+ T+ P- | 729756 | −0.054 | −0.056 | 1656 |
| A- T- P- vs A+ T- P+ | 729756 | −0.071 | −0.096 | 7888 |
| A- T- P- vs A+ T- P- | 729756 | −0.002 | 0.000 | 588 |
| A- T- P- vs A- T+ P+ | 729756 | 0.028 | 0.033 | 1969 |
| A- T- P- vs A- T+ P- | 729756 | −0.096 | −0.112 | 3407 |
| A- T- P- vs A- T- P+ | 729756 | −0.084 | −0.079 | 20499 |
##
## === PARTIAL CORRELATION SUMMARY ===
| Partial Correlation Analysis Summary | |
| Question 0: All Treatments - Controlling for worm counts | |
| Analysis Metric | Count |
|---|---|
| Total partial correlations calculated | 700 |
| Significant partial correlations (FDR < 0.1) | 554 |
| Top 10 taxa by number of correlations | 10 |
| Partial correlations for top 10 taxa | 293 |
##
## Top 10 taxa by number of significant partial correlations:
| Top 10 Taxa by Number of Significant Partial Correlations | ||
| Question 0: All Treatments | ||
| Taxa ID | Number of Correlations | Mean Absolute Correlation |
|---|---|---|
| Culicoidibacter | 65 | 0.527 |
| Tundrisphaera | 46 | 0.576 |
| Polymorphobacter | 33 | 0.659 |
| Rubrivivax | 30 | 0.642 |
| Bosea | 21 | 0.735 |
| Devosia | 21 | 0.648 |
| Gemmobacter | 20 | 0.559 |
| Ensifer | 19 | 0.761 |
| Legionella | 19 | 0.581 |
| Plesiomonas | 19 | 0.350 |
##
## Top 10 taxa by number of significant partial correlations:
| Top 10 Taxa by Number of Significant Partial Correlations by Comparison | |||
| Question 0: All Treatments | |||
| Taxa ID | comparison_name | Number of Correlations | Mean Absolute Correlation |
|---|---|---|---|
| Culicoidibacter | A- T- P- vs A- T- P+ | 47 | 0.488 |
| Culicoidibacter | A- T- P- vs A- T+ P+ | 10 | 0.510 |
| Culicoidibacter | A- T- P- vs A+ T- P- | 4 | 0.846 |
| Culicoidibacter | A- T- P- vs A+ T+ P- | 2 | 0.705 |
| Culicoidibacter | A- T- P- vs A+ T- P+ | 2 | 0.726 |
##
## Top 10 strongest partial correlations (for top 10 taxa only):
| Top 10 Significant Partial Correlations | ||||||||
| Question 0: All Treatments - Top 10 taxa only | ||||||||
| Gene ID | Taxa ID | Partial Correlation | P-value | FDR | Absolute Correlation | Comparison | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|
| LOC137490819 | Culicoidibacter | 0.916 | 4.90 × 10−2 | 7.08 × 10−2 | 0.916 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| gcdhb | Ensifer | 0.907 | 2.50 × 10−2 | 5.10 × 10−2 | 0.907 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| gcdhb | Ensifer | 0.897 | 5.19 × 10−2 | 5.71 × 10−2 | 0.897 | A- T- P- vs A+ T+ P+ | A- T- P- | A+ T+ P+ |
| arglu1a | Bosea | −0.883 | 9.99 × 10−4 | 1.76 × 10−2 | 0.883 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| prdx6 | Bosea | −0.870 | 2.00 × 10−3 | 1.05 × 10−2 | 0.870 | A- T- P- vs A+ T- P+ | A- T- P- | A+ T- P+ |
| gvin1l | Polymorphobacter | 0.869 | 9.99 × 10−4 | 3.84 × 10−3 | 0.869 | A- T- P- vs A+ T+ P+ | A- T- P- | A+ T+ P+ |
| abcc2 | Culicoidibacter | 0.858 | 2.00 × 10−2 | 4.76 × 10−2 | 0.858 | A- T- P- vs A- T+ P+ | A- T- P- | A- T+ P+ |
| med29 | Bosea | −0.857 | 4.00 × 10−3 | 6.89 × 10−3 | 0.857 | A- T- P- vs A+ T+ P+ | A- T- P- | A+ T+ P+ |
| fn3krp | Ensifer | 0.856 | 1.20 × 10−2 | 4.00 × 10−2 | 0.856 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| trpv4 | Culicoidibacter | 0.855 | 4.90 × 10−2 | 7.08 × 10−2 | 0.855 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
##
## Top 10 strongest partial correlations (for top 10 taxa only):
| Top 10 Significant Partial Correlations (Culicoidibacter) | ||||||||
| Question 0: All Treatments - Top 10 features | ||||||||
| Gene ID | Taxa ID | Partial Correlation | P-value | FDR | Absolute Correlation | Comparison | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|
| LOC137490819 | Culicoidibacter | 0.916 | 4.90 × 10−2 | 7.08 × 10−2 | 0.916 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| abcc2 | Culicoidibacter | 0.858 | 2.00 × 10−2 | 4.76 × 10−2 | 0.858 | A- T- P- vs A- T+ P+ | A- T- P- | A- T+ P+ |
| trpv4 | Culicoidibacter | 0.855 | 4.90 × 10−2 | 7.08 × 10−2 | 0.855 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| si:ch73-380l3.2 | Culicoidibacter | 0.850 | 5.09 × 10−2 | 7.08 × 10−2 | 0.850 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| dhrs1 | Culicoidibacter | 0.795 | 9.99 × 10−4 | 7.68 × 10−3 | 0.795 | A- T- P- vs A+ T- P+ | A- T- P- | A+ T- P+ |
| bdh1 | Culicoidibacter | 0.765 | 5.59 × 10−2 | 7.46 × 10−2 | 0.765 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| ano5a | Culicoidibacter | 0.765 | 2.70 × 10−2 | 5.50 × 10−2 | 0.765 | A- T- P- vs A- T+ P+ | A- T- P- | A- T+ P+ |
| mtus1b | Culicoidibacter | 0.759 | 3.10 × 10−2 | 5.16 × 10−2 | 0.759 | A- T- P- vs A- T- P+ | A- T- P- | A- T- P+ |
| ampd2a | Culicoidibacter | 0.748 | 3.90 × 10−2 | 6.71 × 10−2 | 0.748 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| si:dkey-286h2.3 | Culicoidibacter | 0.705 | 1.80 × 10−2 | 4.52 × 10−2 | 0.705 | A- T- P- vs A- T- P+ | A- T- P- | A- T- P+ |
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [50] is not a
## sub-multiple or multiple of the number of rows [38]
## Attempting to load existing results for Question 1...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_parasite_20250630_200828.rds
## Successfully loaded existing results for Question 1.
##
## === QUESTION 1 SUMMARY ===
| Question 1: Parasite Exposure Response | |
| Summary of analysis results | |
| Analysis Metric | Result |
|---|---|
| Question | Parasite Exposure Response |
| Treatment Comparison | A- T- P- vs A- T- P+ |
| Number of Samples | 18 |
| Significant Genes | 3753 |
| Significant Taxa | 30 |
| Significant Correlations | 11681 |
##
## Top 10 strongest correlations:
| gene_id | gene_name | TaxaID | correlation | abs_correlation | p_value | fdr |
|---|---|---|---|---|---|---|
| hephl1b | hephl1b | Paucibacter | −0.926 | 0.926 | 0.00 | 0.00 |
| LOC100006250 | LOC100006250 | Gemmobacter | −0.894 | 0.894 | 1.55 × 10−15 | 6.71 × 10−11 |
| LOC101884777 | LOC101884777 | Vogesella | 0.878 | 0.878 | 1.95 × 10−13 | 5.18 × 10−9 |
| cdk10 | cdk10 | Culicoidibacter | −0.878 | 0.878 | 2.40 × 10−13 | 5.18 × 10−9 |
| nom1 | nom1 | Culicoidibacter | −0.876 | 0.876 | 4.16 × 10−13 | 7.19 × 10−9 |
| prpf31 | prpf31 | Plesiomonas | −0.874 | 0.874 | 7.09 × 10−13 | 1.02 × 10−8 |
| LOC100006250 | LOC100006250 | Paenirhodobacter | −0.871 | 0.871 | 1.21 × 10−12 | 1.46 × 10−8 |
| homezb | homezb | Plesiomonas | −0.870 | 0.870 | 1.53 × 10−12 | 1.46 × 10−8 |
| srpk1b | srpk1b | Culicoidibacter | −0.870 | 0.870 | 1.53 × 10−12 | 1.46 × 10−8 |
| cdk10 | cdk10 | Plesiomonas | −0.863 | 0.863 | 7.99 × 10−12 | 6.89 × 10−8 |
results.parasite$sig_partial_correlations %>%
dplyr::filter(fdr < 0.1) %>%
gt::gt() %>%
gt::fmt_number(
columns = c(correlation, p_value, fdr, abs_correlation),
decimals = 4
)
| gene_id | taxa_id | correlation | p_value | fdr | abs_correlation |
|---|---|---|---|---|---|
| spsb1 | Chitinilyticum | 0.8373 | 0.0010 | 0.0333 | 0.8373 |
| mtus1b | Culicoidibacter | 0.7594 | 0.0370 | 0.0528 | 0.7594 |
| si:dkey-286h2.3 | Culicoidibacter | 0.7045 | 0.0090 | 0.0500 | 0.7045 |
| znf692 | Gemmobacter | −0.6923 | 0.0290 | 0.0507 | 0.6923 |
| rfc3 | Paucibacter | −0.6875 | 0.0040 | 0.0444 | 0.6875 |
| bbc3 | Culicoidibacter | 0.6846 | 0.0160 | 0.0500 | 0.6846 |
| gng10 | Paucibacter | −0.6839 | 0.0010 | 0.0333 | 0.6839 |
| pdk2a | Paucibacter | 0.6745 | 0.0220 | 0.0500 | 0.6745 |
| uqcc6 | Paucibacter | −0.6266 | 0.0040 | 0.0444 | 0.6266 |
| slc25a44a | Culicoidibacter | −0.5864 | 0.0210 | 0.0500 | 0.5864 |
| nrbp2b | Culicoidibacter | 0.5854 | 0.0699 | 0.0874 | 0.5854 |
| sco2 | Tundrisphaera | −0.5594 | 0.0260 | 0.0500 | 0.5594 |
| idh2 | Culicoidibacter | −0.5593 | 0.0140 | 0.0500 | 0.5593 |
| mtrex | Culicoidibacter | −0.5557 | 0.0240 | 0.0500 | 0.5557 |
| ing5b | Culicoidibacter | −0.5511 | 0.0210 | 0.0500 | 0.5511 |
| prpf31 | Culicoidibacter | −0.5418 | 0.0150 | 0.0500 | 0.5418 |
| gtdc1 | Culicoidibacter | −0.5403 | 0.0430 | 0.0581 | 0.5403 |
| LOC100006250 | Gemmobacter | −0.5378 | 0.0060 | 0.0500 | 0.5378 |
| aaas | Culicoidibacter | −0.5357 | 0.0390 | 0.0541 | 0.5357 |
| uchl5 | Culicoidibacter | −0.5234 | 0.0260 | 0.0500 | 0.5234 |
| hephl1b | Paucibacter | −0.5218 | 0.0040 | 0.0444 | 0.5218 |
| uqcc6 | Culicoidibacter | −0.5206 | 0.0340 | 0.0507 | 0.5206 |
| cdk10 | Culicoidibacter | −0.5191 | 0.0250 | 0.0500 | 0.5191 |
| ncbp1 | Culicoidibacter | −0.5160 | 0.0230 | 0.0500 | 0.5160 |
| recql4 | Paucibacter | −0.5148 | 0.0040 | 0.0444 | 0.5148 |
| ciao1 | Culicoidibacter | −0.5084 | 0.0250 | 0.0500 | 0.5084 |
| commd9 | Culicoidibacter | −0.5082 | 0.0130 | 0.0500 | 0.5082 |
| zgc:77262 | Culicoidibacter | −0.5081 | 0.0320 | 0.0507 | 0.5081 |
| srpk1b | Culicoidibacter | −0.5045 | 0.0200 | 0.0500 | 0.5045 |
| rmi1 | Culicoidibacter | −0.5036 | 0.0470 | 0.0618 | 0.5036 |
| haus6 | Culicoidibacter | −0.4997 | 0.0100 | 0.0500 | 0.4997 |
| prps1b | Culicoidibacter | −0.4993 | 0.0330 | 0.0507 | 0.4993 |
| psmc4 | Culicoidibacter | −0.4912 | 0.0280 | 0.0507 | 0.4912 |
| nsfl1c | Culicoidibacter | −0.4905 | 0.0140 | 0.0500 | 0.4905 |
| si:ch211-165i18.2 | Paucibacter | −0.4893 | 0.0010 | 0.0333 | 0.4893 |
| tardbpb | Culicoidibacter | −0.4868 | 0.0170 | 0.0500 | 0.4868 |
| rbm12bb | Culicoidibacter | −0.4792 | 0.0240 | 0.0500 | 0.4792 |
| denr | Culicoidibacter | −0.4761 | 0.0360 | 0.0528 | 0.4761 |
| dmap1 | Culicoidibacter | −0.4684 | 0.0200 | 0.0500 | 0.4684 |
| psmd4a | Culicoidibacter | −0.4671 | 0.0130 | 0.0500 | 0.4671 |
| pigf | Culicoidibacter | −0.4670 | 0.0330 | 0.0507 | 0.4670 |
| psmd6 | Culicoidibacter | −0.4670 | 0.0140 | 0.0500 | 0.4670 |
| fus | Culicoidibacter | −0.4665 | 0.0200 | 0.0500 | 0.4665 |
| psmd1 | Culicoidibacter | −0.4656 | 0.0230 | 0.0500 | 0.4656 |
| psmd2 | Culicoidibacter | −0.4632 | 0.0150 | 0.0500 | 0.4632 |
| ssbp1 | Culicoidibacter | −0.4609 | 0.0559 | 0.0717 | 0.4609 |
| igbp1 | Culicoidibacter | −0.4607 | 0.0110 | 0.0500 | 0.4607 |
| gng10 | Paenirhodobacter | −0.4589 | 0.0020 | 0.0400 | 0.4589 |
| ppp1r11 | Culicoidibacter | −0.4575 | 0.0180 | 0.0500 | 0.4575 |
| tubb4b | Culicoidibacter | −0.4499 | 0.0280 | 0.0507 | 0.4499 |
| lonp1 | Culicoidibacter | −0.4499 | 0.0140 | 0.0500 | 0.4499 |
| gfpt1 | Culicoidibacter | −0.4486 | 0.0400 | 0.0547 | 0.4486 |
| tuba8l4 | Culicoidibacter | −0.4482 | 0.0170 | 0.0500 | 0.4482 |
| psmd3 | Culicoidibacter | −0.4411 | 0.0300 | 0.0507 | 0.4411 |
| imp4 | Culicoidibacter | −0.4375 | 0.0260 | 0.0500 | 0.4375 |
| pfkpa | Culicoidibacter | −0.4332 | 0.0390 | 0.0541 | 0.4332 |
| ctbp1l | Culicoidibacter | −0.4319 | 0.0260 | 0.0500 | 0.4319 |
| nom1 | Culicoidibacter | −0.4299 | 0.0210 | 0.0500 | 0.4299 |
| h2ax | Culicoidibacter | −0.4279 | 0.0460 | 0.0613 | 0.4279 |
| mmp25b | Culicoidibacter | −0.4273 | 0.0310 | 0.0507 | 0.4273 |
| mcts1 | Culicoidibacter | −0.4271 | 0.0500 | 0.0649 | 0.4271 |
| csf3r | Culicoidibacter | −0.4197 | 0.0180 | 0.0500 | 0.4197 |
| zgc:85858 | Culicoidibacter | −0.4182 | 0.0330 | 0.0507 | 0.4182 |
| h3f3a | Culicoidibacter | −0.4170 | 0.0060 | 0.0500 | 0.4170 |
| mrpl33 | Culicoidibacter | −0.4102 | 0.0250 | 0.0500 | 0.4102 |
| arf2b | Culicoidibacter | −0.4096 | 0.0300 | 0.0507 | 0.4096 |
| tars1 | Culicoidibacter | −0.4086 | 0.0340 | 0.0507 | 0.4086 |
| si:ch211-284o19.8 | Culicoidibacter | −0.4082 | 0.0370 | 0.0528 | 0.4082 |
| LOC100006250 | Paenirhodobacter | −0.4073 | 0.0020 | 0.0400 | 0.4073 |
| tubb2b | Culicoidibacter | −0.3988 | 0.0180 | 0.0500 | 0.3988 |
| hephl1b | Paenirhodobacter | −0.3883 | 0.0150 | 0.0500 | 0.3883 |
| anxa5b | Culicoidibacter | −0.3796 | 0.0130 | 0.0500 | 0.3796 |
| armc7 | Paenirhodobacter | −0.3574 | 0.0080 | 0.0500 | 0.3574 |
| agr2 | Culicoidibacter | −0.3543 | 0.0340 | 0.0507 | 0.3543 |
| msraa | Polymorphobacter | −0.3441 | 0.0160 | 0.0500 | 0.3441 |
| LOC101884777 | Vogesella | 0.3276 | 0.0619 | 0.0784 | 0.3276 |
| p2rx4b | Gemmobacter | −0.3085 | 0.0310 | 0.0507 | 0.3085 |
| zgc:172339 | Gemmobacter | −0.2649 | 0.0200 | 0.0500 | 0.2649 |
| LOC137487483 | Paenirhodobacter | −0.2322 | 0.0210 | 0.0500 | 0.2322 |
| pvalb7 | Gemmobacter | −0.2287 | 0.0290 | 0.0507 | 0.2287 |
# Count of significant partial correlations
results.parasite$sig_partial_correlations %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::summarise(
n_significant = n()
) %>%
gt::gt() %>%
gt::tab_header(
title = "Number of Significant Partial Correlations",
subtitle = "Parasite Exposure Response (FDR < 0.1)"
) %>%
gt::cols_label(
n_significant = "Number of Significant Correlations"
)
| Number of Significant Partial Correlations |
| Parasite Exposure Response (FDR < 0.1) |
| Number of Significant Correlations |
|---|
| 80 |
# Count of significant partial correlations by direction
results.parasite$sig_partial_correlations %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::mutate(
direction = dplyr::case_when(
correlation > 0 ~ "Positive",
correlation < 0 ~ "Negative",
TRUE ~ "Zero"
)
) %>%
dplyr::group_by(direction) %>%
dplyr::summarise(
n_correlations = n(),
.groups = "drop"
) %>%
gt::gt() %>%
gt::tab_header(
title = "Significant Partial Correlations by Direction",
subtitle = "Parasite Exposure Response (FDR < 0.1)"
) %>%
gt::cols_label(
direction = "Correlation Direction",
n_correlations = "Number of Correlations"
)
| Significant Partial Correlations by Direction | |
| Parasite Exposure Response (FDR < 0.1) | |
| Correlation Direction | Number of Correlations |
|---|---|
| Negative | 73 |
| Positive | 7 |
##
## === PARTIAL CORRELATION SUMMARY ===
##
## Top 10 strongest partial correlations:
## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [100] is not a
## sub-multiple or multiple of the number of rows [87]
## Attempting to load existing results for Question 2...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_historical_contingency_20250630_200918.rds
## Successfully loaded existing results for Question 2.
##
## === QUESTION 2 SUMMARY ===
| Question 2: Historical Contingency of Parasite Response | |
| Summary of analysis results | |
| Analysis Metric | Result |
|---|---|
| Question | Historical Contingency of Parasite Response |
| Base Treatment | A- T- P+ |
| Comparison Treatments | A+ T- P+, A- T+ P+, A+ T+ P+ |
| Total Samples | 48 |
| Number of Pairwise Comparisons | 3 |
| Pairwise Comparison Details | ||||
| Results for each individual comparison | ||||
| Comparison | N Samples | N Significant Genes | N Significant Taxa | N Significant Correlations |
|---|---|---|---|---|
| A- T- P+ vs A+ T- P+ | 24 | 49 | 48 | 1 |
| A- T- P+ vs A- T+ P+ | 24 | 137 | 16 | 3 |
| A- T- P+ vs A+ T+ P+ | 24 | 18 | 33 | 0 |
##
## Top 10 strongest correlations across all pairwise comparisons:
| gene_id | gene_name | TaxaID | correlation | abs_correlation | p_value | fdr | comparison_name | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|---|
| zmp:0000000629 | zmp:0000000629 | Polymorphobacter | −0.716 | 0.716 | 1.54 × 10−6 | 5.33 × 10−3 | A- T- P+ vs A- T+ P+ | A- T- P+ | A- T+ P+ |
| zmp:0000000629 | zmp:0000000629 | Chryseotalea | −0.683 | 0.683 | 1.16 × 10−5 | 1.37 × 10−2 | A- T- P+ vs A- T+ P+ | A- T- P+ | A- T+ P+ |
| lsm14b | lsm14b | Polymorphobacter | −0.683 | 0.683 | 1.19 × 10−5 | 1.37 × 10−2 | A- T- P+ vs A- T+ P+ | A- T- P+ | A- T+ P+ |
| zranb2 | zranb2 | Uliginosibacterium | 0.682 | 0.682 | 1.22 × 10−5 | 4.39 × 10−2 | A- T- P+ vs A+ T- P+ | A- T- P+ | A+ T- P+ |
## Warning: Removed 994 rows containing non-finite outside the scale range
## (`stat_bin()`).
##
## Correlation summary by comparison:
| comparison_name | n_correlations | mean_correlation | median_correlation | n_significant |
|---|---|---|---|---|
| A- T- P+ vs A+ T+ P+ | 4004 | −0.059 | −0.069 | 0 |
| A- T- P+ vs A+ T- P+ | 4004 | −0.071 | −0.080 | 1 |
| A- T- P+ vs A- T+ P+ | 4004 | −0.033 | −0.038 | 3 |
results.historical_contingency[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
gt::gt() %>%
gt::fmt_number(
columns = c(correlation, p_value, fdr, abs_correlation),
decimals = 4
)
| gene_id | taxa_id | correlation | p_value | fdr | abs_correlation | comparison_name | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|
| zranb2 | Uliginosibacterium | 0.6274 | 0.0240 | 0.0240 | 0.6274 | A- T- P+ vs A+ T- P+ | A- T- P+ | A+ T- P+ |
| lsm14b | Polymorphobacter | −0.3765 | 0.0100 | 0.0300 | 0.3765 | A- T- P+ vs A- T+ P+ | A- T- P+ | A- T+ P+ |
# Count of significant partial correlations
results.historical_contingency[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::group_by(comparison_name) %>%
dplyr::summarise(
n_significant = n()
) %>%
gt::gt() %>%
gt::tab_header(
title = "Number of Significant Partial Correlations",
subtitle = "Historical Contingency of Parasite Response (FDR < 0.1)"
) %>%
gt::cols_label(
n_significant = "Number of Significant Correlations"
)
| Number of Significant Partial Correlations | |
| Historical Contingency of Parasite Response (FDR < 0.1) | |
| comparison_name | Number of Significant Correlations |
|---|---|
| A- T- P+ vs A+ T- P+ | 1 |
| A- T- P+ vs A- T+ P+ | 1 |
# Count of significant partial correlations by direction
results.historical_contingency[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::mutate(
direction = dplyr::case_when(
correlation > 0 ~ "Positive",
correlation < 0 ~ "Negative",
TRUE ~ "Zero"
)
) %>%
dplyr::group_by(comparison_name, direction) %>%
dplyr::summarise(
n_correlations = n()#,
# .groups = "drop"
) %>%
gt::gt() %>%
gt::tab_header(
title = "Significant Partial Correlations by Direction",
subtitle = "Historical Contingency of Parasite Response (FDR < 0.1)"
) %>%
gt::cols_label(
direction = "Correlation Direction",
n_correlations = "Number of Correlations"
)
## `summarise()` has grouped output by 'comparison_name'. You can override using
## the `.groups` argument.
| Significant Partial Correlations by Direction | |
| Historical Contingency of Parasite Response (FDR < 0.1) | |
| Correlation Direction | Number of Correlations |
|---|---|
| A- T- P+ vs A+ T- P+ | |
| Positive | 1 |
| A- T- P+ vs A- T+ P+ | |
| Negative | 1 |
##
## === PARTIAL CORRELATION SUMMARY ===
##
## Top 10 strongest partial correlations:
## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [4] is not a
## sub-multiple or multiple of the number of rows [3]
## Attempting to load existing results for Question 3...
## Loading results from: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results/results_recovery_20250630_201002.rds
## Successfully loaded existing results for Question 3.
##
## === QUESTION 3 SUMMARY ===
| Question 3: Historical Contingency of Recovery | |
| Summary of analysis results | |
| Analysis Metric | Result |
|---|---|
| Question | Historical Contingency of Recovery |
| Base Treatment | A- T- P- |
| Comparison Treatments | A+ T- P-, A- T+ P-, A+ T+ P- |
| Total Samples | 24 |
| Number of Pairwise Comparisons | 3 |
| Pairwise Comparison Details | ||||
| Results for each individual comparison | ||||
| Comparison | N Samples | N Significant Genes | N Significant Taxa | N Significant Correlations |
|---|---|---|---|---|
| A- T- P- vs A+ T- P- | 12 | 35 | 59 | 16 |
| A- T- P- vs A- T+ P- | 12 | 47 | 65 | 45 |
| A- T- P- vs A+ T+ P- | 12 | 117 | 72 | 83 |
##
## Top 10 strongest correlations across all pairwise comparisons:
| gene_id | gene_name | TaxaID | correlation | abs_correlation | p_value | fdr | comparison_name | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|---|
| LOC795984 | LOC795984 | Luteolibacter | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC795984 | LOC795984 | Piscinibacter | 1.000 | 1.000 | 0.00 | 0.00 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| blnk | blnk | Reyranella | −0.940 | 0.940 | 0.00 | 0.00 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| pmaip1 | pmaip1 | Elstera | −0.921 | 0.921 | 7.99 × 10−14 | 1.78 × 10−10 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| elmo2 | elmo2 | Aquihabitans | −0.892 | 0.892 | 4.48 × 10−10 | 7.50 × 10−7 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC101885452 | LOC101885452 | Dinghuibacter | 0.889 | 0.889 | 8.39 × 10−10 | 5.77 × 10−6 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC137496053 | LOC137496053 | Gemmobacter | −0.887 | 0.887 | 1.16 × 10−9 | 3.49 × 10−6 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC141375138 | LOC141375138 | Runella | 0.886 | 0.886 | 1.48 × 10−9 | 3.49 × 10−6 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| zmp:0000000527 | zmp:0000000527 | Reyranella | −0.883 | 0.883 | 2.85 × 10−9 | 5.04 × 10−6 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC137487668 | LOC137487668 | Aquihabitans | 0.871 | 0.871 | 1.93 × 10−8 | 2.59 × 10−5 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
## Warning: Removed 558 rows containing non-finite outside the scale range
## (`stat_bin()`).
##
## Correlation summary by comparison:
| comparison_name | n_correlations | mean_correlation | median_correlation | n_significant |
|---|---|---|---|---|
| A- T- P- vs A+ T+ P- | 7068 | −0.009 | −0.007 | 83 |
| A- T- P- vs A+ T- P- | 7068 | 0.030 | 0.021 | 16 |
| A- T- P- vs A- T+ P- | 7068 | −0.074 | −0.092 | 45 |
# Display all significant partial correlations
results.recovery[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
gt::gt() %>%
gt::fmt_number(
columns = c(correlation, p_value, fdr, abs_correlation),
decimals = 4
)
| gene_id | taxa_id | correlation | p_value | fdr | abs_correlation | comparison_name | base_treatment | comparison_treatment |
|---|---|---|---|---|---|---|---|---|
| adam12b | Flavobacterium | 0.4367 | 0.0040 | 0.0320 | 0.4367 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| pla2g3 | Flavobacterium | 0.4298 | 0.0040 | 0.0320 | 0.4298 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC141379342 | Aquihabitans | 0.2077 | 0.0170 | 0.0906 | 0.2077 | A- T- P- vs A+ T- P- | A- T- P- | A+ T- P- |
| LOC795984 | Luteolibacter | 1.0000 | 0.0010 | 0.0169 | 1.0000 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC795984 | Piscinibacter | 1.0000 | 0.0030 | 0.0169 | 1.0000 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC137489722 | Pseudoxanthomonas | 0.9788 | 0.0400 | 0.0620 | 0.9788 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC141379342 | Pseudoxanthomonas | 0.9769 | 0.0270 | 0.0593 | 0.9769 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC141379345 | Pseudoxanthomonas | 0.9545 | 0.0080 | 0.0327 | 0.9545 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| lhx8a | Rhodococcus | 0.9070 | 0.0360 | 0.0620 | 0.9070 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC101882847 | Pseudoxanthomonas | 0.9014 | 0.0020 | 0.0169 | 0.9014 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC141379353 | Pseudoxanthomonas | 0.8066 | 0.0030 | 0.0169 | 0.8066 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| maml3 | Bosea | −0.7730 | 0.0110 | 0.0380 | 0.7730 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| pmaip1 | Crenobacter | 0.7504 | 0.0509 | 0.0674 | 0.7504 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC137487211 | Flavobacterium | −0.7311 | 0.0390 | 0.0620 | 0.7311 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| maml3 | Pirellula | −0.7069 | 0.0020 | 0.0169 | 0.7069 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| mcfd2 | Bosea | 0.6966 | 0.0130 | 0.0417 | 0.6966 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC110439307 | Pseudoxanthomonas | −0.6611 | 0.0819 | 0.0970 | 0.6611 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC560278 | Sphaerotilus | −0.6609 | 0.0260 | 0.0593 | 0.6609 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| znf1069 | Caulobacter | 0.6585 | 0.0180 | 0.0539 | 0.6585 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| spire1a | Pirellula | −0.6172 | 0.0030 | 0.0169 | 0.6172 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC141379337 | Pseudoxanthomonas | 0.6016 | 0.0030 | 0.0169 | 0.6016 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| ftr34 | Sphaerotilus | −0.5770 | 0.0490 | 0.0668 | 0.5770 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| maml3 | Reyranella | −0.5753 | 0.0010 | 0.0169 | 0.5753 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| pmaip1 | Pseudoxanthomonas | −0.5609 | 0.0290 | 0.0593 | 0.5609 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| pmaip1 | Agitococcus | −0.5603 | 0.0380 | 0.0620 | 0.5603 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC101882702 | Roseomonas | −0.5545 | 0.0579 | 0.0737 | 0.5545 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| si:ch211-217k17.11 | Runella | −0.5458 | 0.0260 | 0.0593 | 0.5458 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC110438438 | Pseudoxanthomonas | 0.5388 | 0.0040 | 0.0200 | 0.5388 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| si:ch211-217k17.11 | Sphaerotilus | −0.5309 | 0.0330 | 0.0611 | 0.5309 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC100001589 | Runella | −0.5034 | 0.0080 | 0.0327 | 0.5034 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| dhx58 | Reyranella | −0.5010 | 0.0110 | 0.0380 | 0.5010 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| dhx58 | Runella | −0.4941 | 0.0220 | 0.0582 | 0.4941 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| pmaip1 | Bdellovibrio | −0.4541 | 0.0420 | 0.0629 | 0.4541 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| si:ch211-15j1.4 | Reyranella | −0.4488 | 0.0290 | 0.0593 | 0.4488 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| si:ch211-15j1.4 | Runella | −0.4367 | 0.0310 | 0.0606 | 0.4367 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC101883008 | Culicoidibacter | −0.4241 | 0.0470 | 0.0668 | 0.4241 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| pmaip1 | Elstera | −0.3968 | 0.0480 | 0.0668 | 0.3968 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| LOC137487211 | Culicoidibacter | −0.3781 | 0.0589 | 0.0737 | 0.3781 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| elmo2 | Aquihabitans | −0.3665 | 0.0340 | 0.0611 | 0.3665 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| si:ch211-15j1.4 | Agitococcus | −0.3400 | 0.0709 | 0.0863 | 0.3400 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| slco1c1 | Aquihabitans | 0.2119 | 0.0210 | 0.0582 | 0.2119 | A- T- P- vs A- T+ P- | A- T- P- | A- T+ P- |
| rergla | Pseudorhodoplanes | 0.9493 | 0.0500 | 0.0846 | 0.9493 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| znf1069 | Legionella | 0.8945 | 0.0390 | 0.0703 | 0.8945 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC141375138 | Runella | 0.8897 | 0.0060 | 0.0415 | 0.8897 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| prag1 | Pirellula | −0.8807 | 0.0170 | 0.0522 | 0.8807 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| zmp:0000000527 | Pirellula | −0.8723 | 0.0070 | 0.0434 | 0.8723 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| cxcl14 | Bosea | −0.8514 | 0.0060 | 0.0415 | 0.8514 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| spire1a | Pirellula | −0.8270 | 0.0060 | 0.0415 | 0.8270 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| blnk | Pirellula | −0.7939 | 0.0100 | 0.0434 | 0.7939 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC100537840 | Pirellula | −0.7683 | 0.0050 | 0.0415 | 0.7683 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| tnfa | Pirellula | −0.7665 | 0.0090 | 0.0434 | 0.7665 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| faah | Pirellula | −0.7363 | 0.0160 | 0.0510 | 0.7363 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| ly6m5 | Flavobacterium | 0.7361 | 0.0160 | 0.0510 | 0.7361 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| vps33a | Bosea | −0.7246 | 0.0040 | 0.0415 | 0.7246 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| rnf213a | Pirellula | −0.7227 | 0.0020 | 0.0415 | 0.7227 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| irf3 | Flavobacterium | 0.7109 | 0.0330 | 0.0684 | 0.7109 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC101883092 | Pirellula | −0.7093 | 0.0250 | 0.0576 | 0.7093 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC103910189 | Pirellula | −0.6807 | 0.0240 | 0.0576 | 0.6807 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| sash1b | Pirellula | −0.6599 | 0.0240 | 0.0576 | 0.6599 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| vim | Bosea | −0.6590 | 0.0110 | 0.0434 | 0.6590 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| pstpip1b | Pirellula | −0.6541 | 0.0230 | 0.0576 | 0.6541 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| hspa8 | Gemmobacter | −0.6432 | 0.0430 | 0.0743 | 0.6432 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| tmem106a | Bosea | −0.6313 | 0.0030 | 0.0415 | 0.6313 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC141385619 | Culicoidibacter | 0.6274 | 0.0080 | 0.0434 | 0.6274 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| zgc:56106 | Runella | −0.5926 | 0.0010 | 0.0415 | 0.5926 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC137496184 | Flavobacterium | −0.5884 | 0.0120 | 0.0452 | 0.5884 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| cxcl14 | Flavobacterium | −0.5850 | 0.0160 | 0.0510 | 0.5850 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| si:ch1073-406l10.2 | Neochlamydia | 0.5778 | 0.0030 | 0.0415 | 0.5778 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| si:ch1073-406l10.2 | Gemmobacter | −0.5757 | 0.0330 | 0.0684 | 0.5757 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| prr5a | Flavobacterium | −0.5724 | 0.0380 | 0.0703 | 0.5724 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC560278 | Luteolibacter | −0.5508 | 0.0210 | 0.0576 | 0.5508 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC141378197 | Runella | −0.5370 | 0.0370 | 0.0703 | 0.5370 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC795984 | Pirellula | 0.5311 | 0.0300 | 0.0672 | 0.5311 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC137496184 | Bosea | −0.5222 | 0.0040 | 0.0415 | 0.5222 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC101883092 | Runella | −0.5080 | 0.0220 | 0.0576 | 0.5080 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC793010 | Dongia | −0.5044 | 0.0360 | 0.0703 | 0.5044 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC137496184 | Gemmobacter | −0.4932 | 0.0060 | 0.0415 | 0.4932 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC100537840 | Runella | −0.4756 | 0.0559 | 0.0892 | 0.4756 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC103910189 | Runella | −0.4714 | 0.0250 | 0.0576 | 0.4714 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC137496053 | Gemmobacter | −0.4630 | 0.0110 | 0.0434 | 0.4630 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| si:ch211-125e6.14 | Tundrisphaera | −0.4618 | 0.0320 | 0.0684 | 0.4618 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| prr5a | Bosea | −0.4563 | 0.0190 | 0.0563 | 0.4563 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| igf2bp2b | Runella | −0.4547 | 0.0210 | 0.0576 | 0.4547 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| ugt5g1 | Bosea | −0.4413 | 0.0110 | 0.0434 | 0.4413 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| ugt5g1 | Flavobacterium | −0.3948 | 0.0410 | 0.0723 | 0.3948 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC101882702 | Neochlamydia | −0.3904 | 0.0639 | 0.0962 | 0.3904 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC108183350 | Luteolibacter | −0.3691 | 0.0350 | 0.0703 | 0.3691 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| blnk | Reyranella | −0.3672 | 0.0390 | 0.0703 | 0.3672 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| ugt5g1 | Neochlamydia | 0.3628 | 0.0030 | 0.0415 | 0.3628 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC100537840 | Reyranella | −0.3400 | 0.0569 | 0.0892 | 0.3400 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| zgc:171506 | Crenobacter | 0.3351 | 0.0090 | 0.0434 | 0.3351 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| zmp:0000000527 | Reyranella | −0.3274 | 0.0130 | 0.0469 | 0.3274 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| rnf213a | Reyranella | −0.3250 | 0.0649 | 0.0962 | 0.3250 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| pld4 | Reyranella | −0.2990 | 0.0529 | 0.0879 | 0.2990 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| prag1 | Reyranella | −0.2889 | 0.0080 | 0.0434 | 0.2889 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| LOC141375335 | Reyranella | −0.2362 | 0.0549 | 0.0892 | 0.2362 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
| igf2bp2b | Reyranella | −0.2110 | 0.0589 | 0.0906 | 0.2110 | A- T- P- vs A+ T+ P- | A- T- P- | A+ T+ P- |
# Count of significant partial correlations
results.recovery[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::group_by(comparison_name) %>%
dplyr::summarise(
n_significant = n()
) %>%
gt::gt() %>%
gt::tab_header(
title = "Number of Significant Partial Correlations",
subtitle = "Historical Contingency of Recovery (FDR < 0.1)"
) %>%
gt::cols_label(
n_significant = "Number of Significant Correlations"
)
| Number of Significant Partial Correlations | |
| Historical Contingency of Recovery (FDR < 0.1) | |
| comparison_name | Number of Significant Correlations |
|---|---|
| A- T- P- vs A+ T+ P- | 56 |
| A- T- P- vs A+ T- P- | 3 |
| A- T- P- vs A- T+ P- | 38 |
# Count of significant partial correlations by direction
results.recovery[["combined_sig_partial_correlations"]] %>%
dplyr::filter(fdr < 0.1) %>%
dplyr::mutate(
direction = dplyr::case_when(
correlation > 0 ~ "Positive",
correlation < 0 ~ "Negative",
TRUE ~ "Zero"
)
) %>%
dplyr::group_by(comparison_name,direction) %>%
dplyr::summarise(
n_correlations = n()#,
# .groups = "drop"
) %>%
gt::gt() %>%
gt::tab_header(
title = "Significant Partial Correlations by Direction",
subtitle = "Historical Contingency of Recovery (FDR < 0.1)"
) %>%
gt::cols_label(
direction = "Correlation Direction",
n_correlations = "Number of Correlations"
)
## `summarise()` has grouped output by 'comparison_name'. You can override using
## the `.groups` argument.
| Significant Partial Correlations by Direction | |
| Historical Contingency of Recovery (FDR < 0.1) | |
| Correlation Direction | Number of Correlations |
|---|---|
| A- T- P- vs A+ T+ P- | |
| Negative | 46 |
| Positive | 10 |
| A- T- P- vs A+ T- P- | |
| Positive | 3 |
| A- T- P- vs A- T+ P- | |
| Negative | 24 |
| Positive | 14 |
##
## === PARTIAL CORRELATION SUMMARY ===
##
## Top 10 strongest partial correlations:
## [1] 50
## Warning in matrix(top_correlations$correlation, nrow =
## length(unique(top_correlations$gene_id)), : data length [144] is not a
## sub-multiple or multiple of the number of rows [78]
| Summary Comparison Across Research Questions | |||||||||
| Integrated DEGxDAT Analysis Results | |||||||||
| Research Question | Treatment Comparison | N Samples | N_Pairwise_Comparisons | N Significant Genes | N Significant Taxa | N Significant Correlations | N Significant Partial Correlations | Mean Correlation | Median Correlation |
|---|---|---|---|---|---|---|---|---|---|
| Parasite Exposure Response | A- T- P- vs A- T- P+ | 18 | 1 | 3753 | 30 | 11681 | 80 | −0.100 | −0.088 |
| Historical Contingency of Parasite Response | A- T- P+ vs (A+ T- P+, A- T+ P+, A+ T+ P+) | 48 | 3 | 612 | 123 | 4 | 2 | −0.055 | −0.063 |
| Historical Contingency of Recovery | A- T- P- vs (A+ T- P-, A- T+ P-, A+ T+ P-) | 24 | 3 | 597 | 219 | 144 | 97 | −0.017 | −0.022 |
##
## === GENE OVERLAP STATISTICS ===
| Gene Overlap Statistics | |
| Distribution of significant genes across research questions | |
| Overlap Category | Number of Genes |
|---|---|
| Total unique genes across all questions | 3948 |
| Genes in Parasite Response only | 3582 |
| Genes in Historical Contingency only | 87 |
| Genes in Recovery only | 108 |
| Genes in Parasite Response and Historical Contingency | 95 |
| Genes in Parasite Response and Recovery | 78 |
| Genes in Historical Contingency and Recovery | 2 |
| Genes in all three | 2 |
##
## === TAXA OVERLAP STATISTICS ===
| Taxa Overlap Statistics | |
| Distribution of significant taxa across research questions | |
| Overlap Category | Number of Taxa |
|---|---|
| Total unique taxa across all questions | 95 |
| Taxa in Parasite Response only | 12 |
| Taxa in Historical Contingency only | 19 |
| Taxa in Recovery only | 37 |
| Taxa in Parasite Response and Historical Contingency | 9 |
| Taxa in Parasite Response and Recovery | 15 |
| Taxa in Historical Contingency and Recovery | 15 |
| Taxa in all three | 6 |
## 'select()' returned 1:many mapping between keys and columns
## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2 of `x` matches multiple rows in `y`.
## ℹ Row 7 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
##
## Processing contrast with 467 genes
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##
## === GO ENRICHMENT - ALL TREATMENTS ===
| Top 10 Enriched GO Terms | |||||
| All Treatments | |||||
| GO ID | GO Term Description | Adjusted P-value | Mean Correlation | N Positive | N Negative |
|---|---|---|---|---|---|
| GO:0006997 | nucleus organization | 8.21 × 10−3 | −0.191 | 2 | 5 |
| GO:0019941 | modification-dependent protein catabolic process | 1.37 × 10−2 | 0.046 | 11 | 11 |
| GO:0043632 | modification-dependent macromolecule catabolic process | 1.37 × 10−2 | 0.046 | 11 | 11 |
| GO:0006511 | ubiquitin-dependent protein catabolic process | 1.71 × 10−2 | 0.069 | 11 | 10 |
| GO:0006998 | nuclear envelope organization | 9.69 × 10−2 | −0.239 | 1 | 3 |
## 'select()' returned 1:many mapping between keys and columns
## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 2 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
##
## Processing contrast with 2272 genes
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##
## === GO ENRICHMENT - PARASITE EXPOSURE RESPONSE ===
| Top 10 Enriched GO Terms | |||||
| Parasite Exposure Response | |||||
| GO ID | GO Term Description | Adjusted P-value | Mean Correlation | N Positive | N Negative |
|---|---|---|---|---|---|
| GO:0006260 | DNA replication | 1.46 × 10−42 | −0.719 | 1 | 82 |
| GO:0007059 | chromosome segregation | 7.90 × 10−38 | −0.717 | 1 | 88 |
| GO:0006974 | DNA damage response | 6.83 × 10−35 | −0.563 | 16 | 124 |
| GO:1903047 | mitotic cell cycle process | 3.76 × 10−33 | −0.644 | 7 | 107 |
| GO:0006281 | DNA repair | 6.48 × 10−32 | −0.601 | 10 | 105 |
| GO:0051276 | chromosome organization | 8.58 × 10−32 | −0.700 | 2 | 95 |
| GO:0006261 | DNA-templated DNA replication | 3.17 × 10−31 | −0.741 | 0 | 55 |
| GO:0098813 | nuclear chromosome segregation | 2.08 × 10−27 | −0.734 | 0 | 59 |
| GO:0000819 | sister chromatid segregation | 2.66 × 10−27 | −0.733 | 0 | 50 |
| GO:0048285 | organelle fission | 4.52 × 10−27 | −0.658 | 4 | 74 |
## 'select()' returned 1:1 mapping between keys and columns
## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2 of `x` matches multiple rows in `y`.
## ℹ Row 2 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
##
## Processing contrast with 3 genes
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##
## === GO ENRICHMENT - HISTORICAL CONTINGENCY OF PARASITE RESPONSE ===
| Top 10 Enriched GO Terms | |||||
| Historical Contingency of Parasite Response | |||||
| GO ID | GO Term Description | Adjusted P-value | Mean Correlation | N Positive | N Negative |
|---|---|---|---|---|---|
| GO:0006952 | defense response | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0009607 | response to biotic stimulus | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0009617 | response to bacterium | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0042742 | defense response to bacterium | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0043207 | response to external biotic stimulus | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0051707 | response to other organism | 5.18 × 10−2 | 0.682 | 1 | 0 |
| GO:0098542 | defense response to other organism | 5.18 × 10−2 | 0.682 | 1 | 0 |
## 'select()' returned 1:1 mapping between keys and columns
## Warning in dplyr::left_join(., entrez_map, by = "gene_id"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
##
## Processing contrast with 60 genes
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##
## === GO ENRICHMENT - HISTORICAL CONTINGENCY OF RECOVERY ===
| Top 10 Enriched GO Terms | |||||
| Historical Contingency of Recovery | |||||
| GO ID | GO Term Description | Adjusted P-value | Mean Correlation | N Positive | N Negative |
|---|---|---|---|---|---|
| GO:0006952 | defense response | 3.02 × 10−2 | −0.527 | 1 | 5 |
| GO:0045109 | intermediate filament organization | 8.60 × 10−2 | 0.043 | 1 | 1 |
# Function to create comprehensive enrichment tables for significant correlations
create_comprehensive_enrichment_tables <- function(results_list, analysis_name) {
# This function creates comprehensive tables of significant gene-taxa correlations
# with GO and KEGG annotations for a given analysis
cat("\n=== Creating comprehensive enrichment tables for:", analysis_name, "===\n")
# Get significant partial correlations
if ("sig_partial_correlations" %in% names(results_list)) {
sig_correlations <- results_list$sig_partial_correlations
} else if ("combined_sig_partial_correlations" %in% names(results_list)) {
sig_correlations <- results_list$combined_sig_partial_correlations
} else {
cat("No significant partial correlations found for", analysis_name, "\n")
return(NULL)
}
if (nrow(sig_correlations) == 0) {
cat("No significant partial correlations found for", analysis_name, "\n")
return(NULL)
}
# Get unique genes from significant correlations
unique_genes <- unique(sig_correlations$gene_id)
cat("Number of unique genes:", length(unique_genes), "\n")
# Map genes to Entrez IDs and get annotations
# First, get Entrez IDs
entrez_ids <- AnnotationDbi::mapIds(
org.Dr.eg.db,
keys = unique_genes,
column = "ENTREZID",
keytype = "SYMBOL",
multiVals = "first"
)
# Then, get gene names
gene_names <- AnnotationDbi::mapIds(
org.Dr.eg.db,
keys = unique_genes,
column = "GENENAME",
keytype = "SYMBOL",
multiVals = "first"
)
# Create annotation dataframe
gene_annotations <- tibble::tibble(
gene_id = unique_genes,
entrez_id = as.character(entrez_ids),
gene_name = gene_names,
symbol = unique_genes # Since we're using SYMBOL as keytype, symbol is the same as gene_id
) %>%
dplyr::filter(!is.na(entrez_id))
# Perform GO enrichment for these genes
if (nrow(gene_annotations) > 0) {
go_enrichment <- enrichGO(
gene = gene_annotations$entrez_id,
OrgDb = org.Dr.eg.db,
keyType = "ENTREZID",
ont = "BP",
pAdjustMethod = "BH",
pvalueCutoff = 0.1,
qvalueCutoff = 0.2,
readable = TRUE
)
if (!is.null(go_enrichment) && nrow(go_enrichment) > 0) {
go_results <- as.data.frame(go_enrichment) %>%
dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
dplyr::mutate(log_padj = -log10(p.adjust))
} else {
go_results <- data.frame()
}
# Perform KEGG enrichment for these genes
kegg_enrichment <- enrichKEGG(
gene = gene_annotations$entrez_id,
organism = 'dre',
keyType = 'ncbi-geneid',
pAdjustMethod = "BH",
pvalueCutoff = 0.1,
qvalueCutoff = 0.2
)
if (!is.null(kegg_enrichment) && nrow(kegg_enrichment) > 0) {
kegg_results <- as.data.frame(kegg_enrichment) %>%
dplyr::select(ID, Description, p.adjust, Count, geneID) %>%
dplyr::mutate(log_padj = -log10(p.adjust))
} else {
kegg_results <- data.frame()
}
} else {
go_results <- data.frame()
kegg_results <- data.frame()
}
# Create comprehensive correlation table with annotations
comprehensive_table <- sig_correlations %>%
dplyr::left_join(gene_annotations, by = "gene_id") %>%
dplyr::mutate(
gene_description = ifelse(!is.na(gene_name), gene_name, gene_id),
analysis = analysis_name
) %>%
dplyr::select(
analysis, gene_id, gene_description, taxa_id, correlation,
p_value, fdr, abs_correlation, entrez_id
) %>%
dplyr::arrange(desc(abs_correlation))
# Add GO term annotations to the comprehensive table
if (nrow(go_results) > 0) {
go_gene_mapping <- go_results %>%
tidyr::separate_rows(geneID, sep = "/") %>%
dplyr::select(ID, Description, geneID) %>%
dplyr::rename(go_id = ID, go_description = Description, gene_symbol = geneID)
comprehensive_table <- comprehensive_table %>%
dplyr::left_join(go_gene_mapping, by = c("gene_id" = "gene_symbol")) %>%
dplyr::group_by(analysis, gene_id, gene_description, taxa_id, correlation,
p_value, fdr, abs_correlation, entrez_id) %>%
dplyr::summarise(
go_terms = paste(unique(go_description), collapse = "; "),
.groups = "drop"
)
} else {
comprehensive_table <- comprehensive_table %>%
dplyr::mutate(go_terms = NA_character_)
}
# Add KEGG pathway annotations to the comprehensive table
if (nrow(kegg_results) > 0) {
kegg_gene_mapping <- kegg_results %>%
tidyr::separate_rows(geneID, sep = "/") %>%
dplyr::select(ID, Description, geneID) %>%
dplyr::rename(kegg_id = ID, kegg_description = Description, gene_symbol = geneID)
comprehensive_table <- comprehensive_table %>%
dplyr::left_join(kegg_gene_mapping, by = c("gene_id" = "gene_symbol")) %>%
dplyr::group_by(analysis, gene_id, gene_description, taxa_id, correlation,
p_value, fdr, abs_correlation, entrez_id, go_terms) %>%
dplyr::summarise(
kegg_pathways = paste(unique(kegg_description), collapse = "; "),
.groups = "drop"
)
} else {
comprehensive_table <- comprehensive_table %>%
dplyr::mutate(kegg_pathways = NA_character_)
}
return(list(
comprehensive_table = comprehensive_table,
go_results = go_results,
kegg_results = kegg_results,
gene_annotations = gene_annotations
))
}
# Function to export results for each analysis
export_analysis_results <- function(results_list, analysis_name, results_dir = RESULTS_DIR) {
# This function exports comprehensive results for a given analysis
cat("\n=== Exporting results for:", analysis_name, "===\n")
# Create timestamp
timestamp <- format(Sys.time(), "%Y%m%d_%H%M%S")
# Create analysis-specific directory
analysis_dir <- file.path(results_dir, analysis_name)
if (!dir.exists(analysis_dir)) {
dir.create(analysis_dir, recursive = TRUE)
}
# Export significant correlations
if ("sig_correlations" %in% names(results_list) && nrow(results_list$sig_correlations) > 0) {
correlations_file <- file.path(analysis_dir, paste0("significant_correlations_", timestamp, ".tsv"))
readr::write_tsv(results_list$sig_correlations, correlations_file)
cat("Exported significant correlations to:", correlations_file, "\n")
}
# Export significant partial correlations
if ("sig_partial_correlations" %in% names(results_list) && nrow(results_list$sig_partial_correlations) > 0) {
partial_correlations_file <- file.path(analysis_dir, paste0("significant_partial_correlations_", timestamp, ".tsv"))
readr::write_tsv(results_list$sig_partial_correlations, partial_correlations_file)
cat("Exported significant partial correlations to:", partial_correlations_file, "\n")
} else if ("combined_sig_partial_correlations" %in% names(results_list) && nrow(results_list$combined_sig_partial_correlations) > 0) {
partial_correlations_file <- file.path(analysis_dir, paste0("significant_partial_correlations_", timestamp, ".tsv"))
readr::write_tsv(results_list$combined_sig_partial_correlations, partial_correlations_file)
cat("Exported significant partial correlations to:", partial_correlations_file, "\n")
}
# Export all correlations
if ("correlation_results" %in% names(results_list) && nrow(results_list$correlation_results) > 0) {
all_correlations_file <- file.path(analysis_dir, paste0("all_correlations_", timestamp, ".tsv"))
readr::write_tsv(results_list$correlation_results, all_correlations_file)
cat("Exported all correlations to:", all_correlations_file, "\n")
} else if ("combined_all_correlations" %in% names(results_list) && nrow(results_list$combined_all_correlations) > 0) {
all_correlations_file <- file.path(analysis_dir, paste0("all_correlations_", timestamp, ".tsv"))
readr::write_tsv(results_list$combined_all_correlations, all_correlations_file)
cat("Exported all correlations to:", all_correlations_file, "\n")
}
# Export DEG results
if ("deg_sig" %in% names(results_list) && nrow(results_list$deg_sig) > 0) {
deg_file <- file.path(analysis_dir, paste0("significant_genes_", timestamp, ".tsv"))
readr::write_tsv(results_list$deg_sig, deg_file)
cat("Exported significant genes to:", deg_file, "\n")
} else if ("combined_deg" %in% names(results_list) && nrow(results_list$combined_deg) > 0) {
deg_file <- file.path(analysis_dir, paste0("significant_genes_", timestamp, ".tsv"))
readr::write_tsv(results_list$combined_deg, deg_file)
cat("Exported significant genes to:", deg_file, "\n")
}
# Export DAT results
if ("dat_sig" %in% names(results_list) && nrow(results_list$dat_sig) > 0) {
dat_file <- file.path(analysis_dir, paste0("significant_taxa_", timestamp, ".tsv"))
readr::write_tsv(results_list$dat_sig, dat_file)
cat("Exported significant taxa to:", dat_file, "\n")
} else if ("combined_dat" %in% names(results_list) && nrow(results_list$combined_dat) > 0) {
dat_file <- file.path(analysis_dir, paste0("significant_taxa_", timestamp, ".tsv"))
readr::write_tsv(results_list$combined_dat, dat_file)
cat("Exported significant taxa to:", dat_file, "\n")
}
# Create and export comprehensive enrichment tables
enrichment_results <- create_comprehensive_enrichment_tables(results_list, analysis_name)
if (!is.null(enrichment_results)) {
# Export comprehensive table
comprehensive_file <- file.path(analysis_dir, paste0("comprehensive_gene_taxa_correlations_", timestamp, ".tsv"))
readr::write_tsv(enrichment_results$comprehensive_table, comprehensive_file)
cat("Exported comprehensive gene-taxa correlations to:", comprehensive_file, "\n")
# Export GO results
if (nrow(enrichment_results$go_results) > 0) {
go_file <- file.path(analysis_dir, paste0("go_enrichment_", timestamp, ".tsv"))
readr::write_tsv(enrichment_results$go_results, go_file)
cat("Exported GO enrichment results to:", go_file, "\n")
}
# Export KEGG results
if (nrow(enrichment_results$kegg_results) > 0) {
kegg_file <- file.path(analysis_dir, paste0("kegg_enrichment_", timestamp, ".tsv"))
readr::write_tsv(enrichment_results$kegg_results, kegg_file)
cat("Exported KEGG enrichment results to:", kegg_file, "\n")
}
# Export gene annotations
if (nrow(enrichment_results$gene_annotations) > 0) {
annotations_file <- file.path(analysis_dir, paste0("gene_annotations_", timestamp, ".tsv"))
readr::write_tsv(enrichment_results$gene_annotations, annotations_file)
cat("Exported gene annotations to:", annotations_file, "\n")
}
}
cat("Export complete for:", analysis_name, "\n")
return(enrichment_results)
}
##
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 0: ALL TREATMENTS ===
##
## === Creating comprehensive enrichment tables for: All_Treatments ===
## Number of unique genes: 501
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Reading KEGG annotation online: "https://rest.kegg.jp/link/dre/pathway"...
## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/dre"...
## Reading KEGG annotation online: "https://rest.kegg.jp/conv/ncbi-geneid/dre"...
## Warning in dplyr::left_join(., go_gene_mapping, by = c(gene_id = "gene_symbol")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 22 of `x` matches multiple rows in `y`.
## ℹ Row 17 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
## $comprehensive_table
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## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene Description">Gene Description</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Taxa ID">Taxa ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Correlation">Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="P-value">P-value</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="FDR">FDR</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Abs Correlation">Abs Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Entrez ID">Entrez ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Terms">GO Terms</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG Pathways">KEGG Pathways</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.538</td>
## <td headers="p_value" class="gt_row gt_right">1.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.33 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.538</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.407</td>
## <td headers="p_value" class="gt_row gt_right">3.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.31 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.407</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100148399</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100148399</td>
## <td headers="taxa_id" class="gt_row gt_left">Ensifer</td>
## <td headers="correlation" class="gt_row gt_right">0.620</td>
## <td headers="p_value" class="gt_row gt_right">6.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.89 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.620</td>
## <td headers="entrez_id" class="gt_row gt_right">100148399</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100149761</td>
## <td headers="gene_description" class="gt_row gt_left">dispanin subfamily A member 2b-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Crenobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.672</td>
## <td headers="p_value" class="gt_row gt_right">4.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.672</td>
## <td headers="entrez_id" class="gt_row gt_right">100149761</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100149991</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase TRIM39-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Tundrisphaera</td>
## <td headers="correlation" class="gt_row gt_right">−0.606</td>
## <td headers="p_value" class="gt_row gt_right">3.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.606</td>
## <td headers="entrez_id" class="gt_row gt_right">100149991</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100150003</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100150003</td>
## <td headers="taxa_id" class="gt_row gt_left">Polymorphobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.709</td>
## <td headers="p_value" class="gt_row gt_right">1.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.84 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.709</td>
## <td headers="entrez_id" class="gt_row gt_right">100150003</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100150882</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100150882</td>
## <td headers="taxa_id" class="gt_row gt_left">Polymorphobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.798</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.14 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.798</td>
## <td headers="entrez_id" class="gt_row gt_right">100150882</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100330485</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC100330485</td>
## <td headers="taxa_id" class="gt_row gt_left">Tundrisphaera</td>
## <td headers="correlation" class="gt_row gt_right">−0.546</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.08 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.546</td>
## <td headers="entrez_id" class="gt_row gt_right">100330485</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100331609</td>
## <td headers="gene_description" class="gt_row gt_left">GTPase IMAP family member 8-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.671</td>
## <td headers="p_value" class="gt_row gt_right">1.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.671</td>
## <td headers="entrez_id" class="gt_row gt_right">100331609</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100332293</td>
## <td headers="gene_description" class="gt_row gt_left">WD repeat domain phosphoinositide-interacting protein 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">0.523</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.22 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.523</td>
## <td headers="entrez_id" class="gt_row gt_right">100332293</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100334101</td>
## <td headers="gene_description" class="gt_row gt_left">protein NLRC3-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Devosia</td>
## <td headers="correlation" class="gt_row gt_right">0.801</td>
## <td headers="p_value" class="gt_row gt_right">5.09 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.09 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.801</td>
## <td headers="entrez_id" class="gt_row gt_right">100334101</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Aquihabitans</td>
## <td headers="correlation" class="gt_row gt_right">0.813</td>
## <td headers="p_value" class="gt_row gt_right">2.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.813</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Bradyrhizobium</td>
## <td headers="correlation" class="gt_row gt_right">0.805</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.805</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Mycobacterium</td>
## <td headers="correlation" class="gt_row gt_right">0.889</td>
## <td headers="p_value" class="gt_row gt_right">2.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.78 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.889</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882521</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101882521</td>
## <td headers="taxa_id" class="gt_row gt_left">Nocardia</td>
## <td headers="correlation" class="gt_row gt_right">0.737</td>
## <td headers="p_value" class="gt_row gt_right">5.39 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.29 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.737</td>
## <td headers="entrez_id" class="gt_row gt_right">101882521</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Hyphomicrobium</td>
## <td headers="correlation" class="gt_row gt_right">−0.359</td>
## <td headers="p_value" class="gt_row gt_right">6.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.359</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Mycobacterium</td>
## <td headers="correlation" class="gt_row gt_right">−0.444</td>
## <td headers="p_value" class="gt_row gt_right">3.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.85 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.444</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Paucibacter</td>
## <td headers="correlation" class="gt_row gt_right">0.825</td>
## <td headers="p_value" class="gt_row gt_right">6.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.04 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.825</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883939</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC101883939</td>
## <td headers="taxa_id" class="gt_row gt_left">Vibrio</td>
## <td headers="correlation" class="gt_row gt_right">0.504</td>
## <td headers="p_value" class="gt_row gt_right">9.99 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.68 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.504</td>
## <td headers="entrez_id" class="gt_row gt_right">101883939</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">All_Treatments</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101884777</td>
## <td headers="gene_description" class="gt_row gt_left">butyrophilin-like protein 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Vogesella</td>
## <td headers="correlation" class="gt_row gt_right">0.328</td>
## <td headers="p_value" class="gt_row gt_right">6.29 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.328</td>
## <td headers="entrez_id" class="gt_row gt_right">101884777</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## </tbody>
##
##
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## <td colspan="6" class="gt_heading gt_title gt_font_normal" style>Gene Ontology Enrichment Results</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO ID">GO ID</th>
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## <tr><td headers="ID" class="gt_row gt_left">GO:0006997</td>
## <td headers="Description" class="gt_row gt_left">nucleus organization</td>
## <td headers="p.adjust" class="gt_row gt_right">8.21 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_left">wrap53/chmp2a/bend3/nsfl1c/bmb/coil/lmnl3</td>
## <td headers="log_padj" class="gt_row gt_right">2.09</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0019941</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">22</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/lonp1/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.86</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0043632</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent macromolecule catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">22</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/lonp1/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.86</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006511</td>
## <td headers="Description" class="gt_row gt_left">ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">1.71 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">21</td>
## <td headers="geneID" class="gt_row gt_left">uchl5/psmd4a/psmd6/erlec1/ufl1/ube2h/rnf111/prkn/ube2d2/ube2d2l/nsfl1c/psmd11b/psmc3/uba7/uba1/herc56.2/psma6a/usp11/psmc1a/psmc2/usp38</td>
## <td headers="log_padj" class="gt_row gt_right">1.77</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006998</td>
## <td headers="Description" class="gt_row gt_left">nuclear envelope organization</td>
## <td headers="p.adjust" class="gt_row gt_right">9.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">chmp2a/nsfl1c/bmb/lmnl3</td>
## <td headers="log_padj" class="gt_row gt_right">1.01</td></tr>
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##
##
## </table>
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##
## $kegg_table
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## <tr class="gt_heading">
## <td colspan="6" class="gt_heading gt_title gt_font_normal" style>KEGG Pathway Enrichment Results</td>
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## <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
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## <tr><td headers="ID" class="gt_row gt_left">dre03050</td>
## <td headers="Description" class="gt_row gt_left">Proteasome</td>
## <td headers="p.adjust" class="gt_row gt_right">1.10 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">8</td>
## <td headers="geneID" class="gt_row gt_right">415202/393261/393518/322265/321947/171585/336786/393941</td>
## <td headers="log_padj" class="gt_row gt_right">3.96</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04210</td>
## <td headers="Description" class="gt_row gt_left">Apoptosis</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_right">393154/561021/751765/567460/794666/195817/563053/552927/791531/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04621</td>
## <td headers="Description" class="gt_row gt_left">NOD-like receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">9</td>
## <td headers="geneID" class="gt_row gt_right">492489/393620/561021/100149641/100150844/567460/794666/100334992/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04981</td>
## <td headers="Description" class="gt_row gt_left">Folate transport and metabolism</td>
## <td headers="p.adjust" class="gt_row gt_right">2.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_right">323301/393561/100073336/404609</td>
## <td headers="log_padj" class="gt_row gt_right">1.61</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04625</td>
## <td headers="Description" class="gt_row gt_left">C-type lectin receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_right">192322/561021/100150844/565241/794666/246227/58022</td>
## <td headers="log_padj" class="gt_row gt_right">1.30</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04146</td>
## <td headers="Description" class="gt_row gt_left">Peroxisome</td>
## <td headers="p.adjust" class="gt_row gt_right">5.49 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_right">566378/573990/566863/100034578/100037349/334644</td>
## <td headers="log_padj" class="gt_row gt_right">1.26</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre03410</td>
## <td headers="Description" class="gt_row gt_left">Base excision repair</td>
## <td headers="p.adjust" class="gt_row gt_right">7.87 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_right">259256/550335/393949/571485</td>
## <td headers="log_padj" class="gt_row gt_right">1.10</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre00140</td>
## <td headers="Description" class="gt_row gt_left">Steroid hormone biosynthesis</td>
## <td headers="p.adjust" class="gt_row gt_right">8.25 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
## <td headers="geneID" class="gt_row gt_right">324340/100379286/100415797/678597/334098</td>
## <td headers="log_padj" class="gt_row gt_right">1.08</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04120</td>
## <td headers="Description" class="gt_row gt_left">Ubiquitin mediated proteolysis</td>
## <td headers="p.adjust" class="gt_row gt_right">8.25 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_right">368425/550328/393934/335444/100001302/406335/565383</td>
## <td headers="log_padj" class="gt_row gt_right">1.08</td></tr>
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## padding-bottom: 8px;
## padding-left: 5px;
## padding-right: 5px;
## }
##
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## border-top-style: solid;
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##
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##
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## padding-right: 5px;
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## padding-bottom: 8px;
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## }
##
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## border-bottom-width: 2px;
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## text-indent: 25px;
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="2" class="gt_heading gt_title gt_font_normal" style>Summary Statistics</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>All Treatments Analysis</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Metric">Metric</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Value">Value</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">554</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">501</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">63</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">5</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">9</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.606</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.609</td></tr>
## </tbody>
##
##
## </table>
## </div>
##
## Top 20 significant gene-taxa correlations with annotations:
##
## Top 15 enriched GO terms:
##
## Top 15 enriched KEGG pathways:
| Top 15 Enriched KEGG Pathways | |||||
| All Treatments Analysis | |||||
| KEGG ID | KEGG Pathway Description | Adjusted P-value | Gene Count | Gene IDs | -log10(adj p-value) |
|---|---|---|---|---|---|
| dre03050 | Proteasome | 1.10 × 10−4 | 8 | 415202/393261/393518/322265/321947/171585/336786/393941 | 3.957 |
| dre04210 | Apoptosis | 2.47 × 10−2 | 10 | 393154/561021/751765/567460/794666/195817/563053/552927/791531/58022 | 1.608 |
| dre04621 | NOD-like receptor signaling pathway | 2.47 × 10−2 | 9 | 492489/393620/561021/100149641/100150844/567460/794666/100334992/58022 | 1.608 |
| dre04981 | Folate transport and metabolism | 2.47 × 10−2 | 4 | 323301/393561/100073336/404609 | 1.608 |
| dre04625 | C-type lectin receptor signaling pathway | 5.07 × 10−2 | 7 | 192322/561021/100150844/565241/794666/246227/58022 | 1.295 |
| dre04146 | Peroxisome | 5.49 × 10−2 | 6 | 566378/573990/566863/100034578/100037349/334644 | 1.260 |
| dre03410 | Base excision repair | 7.87 × 10−2 | 4 | 259256/550335/393949/571485 | 1.104 |
| dre00140 | Steroid hormone biosynthesis | 8.25 × 10−2 | 5 | 324340/100379286/100415797/678597/334098 | 1.084 |
| dre04120 | Ubiquitin mediated proteolysis | 8.25 × 10−2 | 7 | 368425/550328/393934/335444/100001302/406335/565383 | 1.084 |
##
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 1: PARASITE EXPOSURE RESPONSE ===
##
## === Creating comprehensive enrichment tables for: Parasite_Exposure_Response ===
## Number of unique genes: 76
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in dplyr::left_join(., go_gene_mapping, by = c(gene_id = "gene_symbol")): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 59 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.
## $comprehensive_table
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## </style>
## <table class="gt_table" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
## <thead>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene Description">Gene Description</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Taxa ID">Taxa ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Correlation">Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="P-value">P-value</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="FDR">FDR</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Abs Correlation">Abs Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Entrez ID">Entrez ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Terms">GO Terms</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG Pathways">KEGG Pathways</th>
## </tr>
## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.538</td>
## <td headers="p_value" class="gt_row gt_right">5.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.538</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100006250</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A2-A-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.407</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.407</td>
## <td headers="entrez_id" class="gt_row gt_right">100006250</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101884777</td>
## <td headers="gene_description" class="gt_row gt_left">butyrophilin-like protein 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Vogesella</td>
## <td headers="correlation" class="gt_row gt_right">0.328</td>
## <td headers="p_value" class="gt_row gt_right">6.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.84 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.328</td>
## <td headers="entrez_id" class="gt_row gt_right">101884777</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487483</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487483</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.232</td>
## <td headers="p_value" class="gt_row gt_right">2.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.232</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">aaas</td>
## <td headers="gene_description" class="gt_row gt_left">achalasia, adrenocortical insufficiency, alacrimia</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.536</td>
## <td headers="p_value" class="gt_row gt_right">3.90 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.41 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.536</td>
## <td headers="entrez_id" class="gt_row gt_right">378454</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">agr2</td>
## <td headers="gene_description" class="gt_row gt_left">anterior gradient 2</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.354</td>
## <td headers="p_value" class="gt_row gt_right">3.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.354</td>
## <td headers="entrez_id" class="gt_row gt_right">335616</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">anxa5b</td>
## <td headers="gene_description" class="gt_row gt_left">annexin A5b</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.380</td>
## <td headers="p_value" class="gt_row gt_right">1.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.380</td>
## <td headers="entrez_id" class="gt_row gt_right">337132</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">arf2b</td>
## <td headers="gene_description" class="gt_row gt_left">ADP-ribosylation factor 2b</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.410</td>
## <td headers="p_value" class="gt_row gt_right">3.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.410</td>
## <td headers="entrez_id" class="gt_row gt_right">327026</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">armc7</td>
## <td headers="gene_description" class="gt_row gt_left">armadillo repeat containing 7</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.357</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.357</td>
## <td headers="entrez_id" class="gt_row gt_right">767659</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">bbc3</td>
## <td headers="gene_description" class="gt_row gt_left">BCL2 binding component 3</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">0.685</td>
## <td headers="p_value" class="gt_row gt_right">1.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.685</td>
## <td headers="entrez_id" class="gt_row gt_right">751763</td>
## <td headers="go_terms" class="gt_row gt_left">mitochondrion organization</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">cdk10</td>
## <td headers="gene_description" class="gt_row gt_left">cyclin dependent kinase 10</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.519</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.519</td>
## <td headers="entrez_id" class="gt_row gt_right">550285</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">ciao1</td>
## <td headers="gene_description" class="gt_row gt_left">cytosolic iron-sulfur assembly component 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">795104</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">commd9</td>
## <td headers="gene_description" class="gt_row gt_left">COMM domain containing 9</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">1.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">562660</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">csf3r</td>
## <td headers="gene_description" class="gt_row gt_left">colony stimulating factor 3 receptor</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.420</td>
## <td headers="p_value" class="gt_row gt_right">1.80 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.420</td>
## <td headers="entrez_id" class="gt_row gt_right">100134935</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">ctbp1l</td>
## <td headers="gene_description" class="gt_row gt_left">ctbp1l</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.432</td>
## <td headers="p_value" class="gt_row gt_right">2.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.432</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">denr</td>
## <td headers="gene_description" class="gt_row gt_left">density-regulated protein</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.476</td>
## <td headers="p_value" class="gt_row gt_right">3.60 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.28 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.476</td>
## <td headers="entrez_id" class="gt_row gt_right">436970</td>
## <td headers="go_terms" class="gt_row gt_left">ribonucleoprotein complex biogenesis; protein-RNA complex assembly; protein-RNA complex organization</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">dmap1</td>
## <td headers="gene_description" class="gt_row gt_left">DNA methyltransferase 1 associated protein 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.468</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.468</td>
## <td headers="entrez_id" class="gt_row gt_right">393225</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">fus</td>
## <td headers="gene_description" class="gt_row gt_left">FUS RNA binding protein</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.466</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.466</td>
## <td headers="entrez_id" class="gt_row gt_right">394058</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">gfpt1</td>
## <td headers="gene_description" class="gt_row gt_left">glutamine--fructose-6-phosphate transaminase 1</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.449</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.449</td>
## <td headers="entrez_id" class="gt_row gt_right">567861</td>
## <td headers="go_terms" class="gt_row gt_left">fructose 6-phosphate metabolic process</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Parasite_Exposure_Response</td>
## <td headers="gene_id" class="gt_row gt_left">gng10</td>
## <td headers="gene_description" class="gt_row gt_left">guanine nucleotide binding protein (G protein), gamma 10</td>
## <td headers="taxa_id" class="gt_row gt_left">Paenirhodobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.459</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.459</td>
## <td headers="entrez_id" class="gt_row gt_right">796780</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
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##
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## <td colspan="6" class="gt_heading gt_title gt_font_normal" style>Gene Ontology Enrichment Results</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO ID">GO ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Term Description">GO Term Description</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Adjusted P-value">Adjusted P-value</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene Count">Gene Count</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene IDs">Gene IDs</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="-log10(adj. P-value)">-log10(adj. P-value)</th>
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## <tbody class="gt_table_body">
## <tr><td headers="ID" class="gt_row gt_left">GO:0019941</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">8.75 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/lonp1/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">3.058</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0043632</td>
## <td headers="Description" class="gt_row gt_left">modification-dependent macromolecule catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">8.75 × 10<sup style='font-size: 65%;'>−4</sup></td>
## <td headers="Count" class="gt_row gt_right">10</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/lonp1/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">3.058</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006511</td>
## <td headers="Description" class="gt_row gt_left">ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">3.08 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="Count" class="gt_row gt_right">9</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/uchl5/psmc4/nsfl1c/psmd4a/psmd6/psmd1/ppp1r11/psmd3</td>
## <td headers="log_padj" class="gt_row gt_right">2.511</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0022613</td>
## <td headers="Description" class="gt_row gt_left">ribonucleoprotein complex biogenesis</td>
## <td headers="p.adjust" class="gt_row gt_right">1.67 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">7</td>
## <td headers="geneID" class="gt_row gt_left">mtrex/prpf31/srpk1b/denr/imp4/nom1/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.778</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0043161</td>
## <td headers="Description" class="gt_row gt_left">proteasome-mediated ubiquitin-dependent protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">2.55 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/psmc4/nsfl1c/psmd4a/psmd6/psmd1</td>
## <td headers="log_padj" class="gt_row gt_right">1.594</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0010498</td>
## <td headers="Description" class="gt_row gt_left">proteasomal protein catabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">4.05 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_left">spsb1/psmc4/nsfl1c/psmd4a/psmd6/psmd1</td>
## <td headers="log_padj" class="gt_row gt_right">1.392</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006002</td>
## <td headers="Description" class="gt_row gt_left">fructose 6-phosphate metabolic process</td>
## <td headers="p.adjust" class="gt_row gt_right">4.05 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_left">gfpt1/pfkpa</td>
## <td headers="log_padj" class="gt_row gt_right">1.392</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0022618</td>
## <td headers="Description" class="gt_row gt_left">protein-RNA complex assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.08 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">prpf31/srpk1b/denr/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.216</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0071826</td>
## <td headers="Description" class="gt_row gt_left">protein-RNA complex organization</td>
## <td headers="p.adjust" class="gt_row gt_right">6.22 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">prpf31/srpk1b/denr/mcts1</td>
## <td headers="log_padj" class="gt_row gt_right">1.206</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0033617</td>
## <td headers="Description" class="gt_row gt_left">mitochondrial cytochrome c oxidase assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_left">uqcc6/sco2</td>
## <td headers="log_padj" class="gt_row gt_right">1.201</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006260</td>
## <td headers="Description" class="gt_row gt_left">DNA replication</td>
## <td headers="p.adjust" class="gt_row gt_right">6.30 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">4</td>
## <td headers="geneID" class="gt_row gt_left">rfc3/recql4/rmi1/ssbp1</td>
## <td headers="log_padj" class="gt_row gt_right">1.201</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0008535</td>
## <td headers="Description" class="gt_row gt_left">respiratory chain complex IV assembly</td>
## <td headers="p.adjust" class="gt_row gt_right">6.47 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
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## <td headers="p.adjust" class="gt_row gt_right">7.65 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
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## <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
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## <tr><td headers="ID" class="gt_row gt_left">dre03050</td>
## <td headers="Description" class="gt_row gt_left">Proteasome</td>
## <td headers="p.adjust" class="gt_row gt_right">8.10 × 10<sup style='font-size: 65%;'>−7</sup></td>
## <td headers="Count" class="gt_row gt_right">6</td>
## <td headers="geneID" class="gt_row gt_right">326884/415202/393261/554967/393518/393544</td>
## <td headers="log_padj" class="gt_row gt_right">6.091</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre03430</td>
## <td headers="Description" class="gt_row gt_left">Mismatch repair</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">259256/550504</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre01230</td>
## <td headers="Description" class="gt_row gt_left">Biosynthesis of amino acids</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">386951/560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre05132</td>
## <td headers="Description" class="gt_row gt_left">Salmonella infection</td>
## <td headers="p.adjust" class="gt_row gt_right">3.61 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">5</td>
## <td headers="geneID" class="gt_row gt_right">100006250/336681/393154/327026/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.443</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre00030</td>
## <td headers="Description" class="gt_row gt_left">Pentose phosphate pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre03030</td>
## <td headers="Description" class="gt_row gt_left">DNA replication</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">259256/550504</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04540</td>
## <td headers="Description" class="gt_row gt_left">Gap junction</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">336681/393154/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre03082</td>
## <td headers="Description" class="gt_row gt_left">ATP-dependent chromatin remodeling</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">406357/393225/394048</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre01200</td>
## <td headers="Description" class="gt_row gt_left">Carbon metabolism</td>
## <td headers="p.adjust" class="gt_row gt_right">4.07 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">386951/560827/560944</td>
## <td headers="log_padj" class="gt_row gt_right">1.390</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04145</td>
## <td headers="Description" class="gt_row gt_left">Phagosome</td>
## <td headers="p.adjust" class="gt_row gt_right">7.95 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">336681/393154/641421</td>
## <td headers="log_padj" class="gt_row gt_right">1.100</td></tr>
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## <tr class="gt_heading">
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## </tr>
## <tr class="gt_heading">
## <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Parasite Exposure Response</td>
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## <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">80</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">76</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">8</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">13</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">10</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.484</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.467</td></tr>
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##
##
## </table>
## </div>
##
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 2: HISTORICAL CONTINGENCY OF PARASITE RESPONSE ===
##
## === Creating comprehensive enrichment tables for: Historical_Contingency_Parasite_Response ===
## Number of unique genes: 2
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## --> No gene can be mapped....
## --> Expected input gene ID: 336606,553401,393668,393461,368912,450002
## --> return NULL...
## $comprehensive_table
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## <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Parasite Response</td>
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## <td headers="gene_id" class="gt_row gt_left">lsm14b</td>
## <td headers="gene_description" class="gt_row gt_left">LSM family member 14B</td>
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## <td headers="correlation" class="gt_row gt_right">−0.377</td>
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## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Parasite_Response</td>
## <td headers="gene_id" class="gt_row gt_left">zranb2</td>
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## <td headers="correlation" class="gt_row gt_right">0.627</td>
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## <td headers="go_terms" class="gt_row gt_left">defense response to bacterium; response to bacterium; defense response to other organism; response to external biotic stimulus; response to other organism; response to biotic stimulus; defense response</td>
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## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0043207</td>
## <td headers="Description" class="gt_row gt_left">response to external biotic stimulus</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0051707</td>
## <td headers="Description" class="gt_row gt_left">response to other organism</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0009607</td>
## <td headers="Description" class="gt_row gt_left">response to biotic stimulus</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">1</td>
## <td headers="geneID" class="gt_row gt_left">zranb2</td>
## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">GO:0006952</td>
## <td headers="Description" class="gt_row gt_left">defense response</td>
## <td headers="p.adjust" class="gt_row gt_right">5.18 × 10<sup style='font-size: 65%;'>−2</sup></td>
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## <td headers="log_padj" class="gt_row gt_right">1.286</td></tr>
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##
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## <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Parasite Response</td>
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## <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">2</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">2</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">2</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">7</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">0</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.502</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
## <td headers="Value" class="gt_row gt_right">0.502</td></tr>
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##
##
## </table>
## </div>
##
## === COMPREHENSIVE ENRICHMENT ANALYSIS - QUESTION 3: HISTORICAL CONTINGENCY OF RECOVERY ===
##
## === Creating comprehensive enrichment tables for: Historical_Contingency_Recovery ===
## Number of unique genes: 63
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
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## <thead>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_title gt_font_normal" style>Top 20 Significant Gene-Taxa Correlations</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="11" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Recovery</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Analysis">Analysis</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene ID">Gene ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Gene Description">Gene Description</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Taxa ID">Taxa ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Correlation">Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="P-value">P-value</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="FDR">FDR</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Abs Correlation">Abs Correlation</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Entrez ID">Entrez ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="GO Terms">GO Terms</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG Pathways">KEGG Pathways</th>
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## </thead>
## <tbody class="gt_table_body">
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100001589</td>
## <td headers="gene_description" class="gt_row gt_left">tripartite motif-containing protein 47-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.503</td>
## <td headers="p_value" class="gt_row gt_right">7.99 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">3.27 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.503</td>
## <td headers="entrez_id" class="gt_row gt_right">100001589</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.768</td>
## <td headers="p_value" class="gt_row gt_right">5.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.15 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.768</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Reyranella</td>
## <td headers="correlation" class="gt_row gt_right">−0.340</td>
## <td headers="p_value" class="gt_row gt_right">5.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.92 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.340</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC100537840</td>
## <td headers="gene_description" class="gt_row gt_left">E3 ubiquitin-protein ligase rnf213-alpha-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.476</td>
## <td headers="p_value" class="gt_row gt_right">5.59 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">8.92 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.476</td>
## <td headers="entrez_id" class="gt_row gt_right">100537840</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Neochlamydia</td>
## <td headers="correlation" class="gt_row gt_right">−0.390</td>
## <td headers="p_value" class="gt_row gt_right">6.39 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">9.62 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.390</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882702</td>
## <td headers="gene_description" class="gt_row gt_left">golgin subfamily A member 6-like protein 22</td>
## <td headers="taxa_id" class="gt_row gt_left">Roseomonas</td>
## <td headers="correlation" class="gt_row gt_right">−0.555</td>
## <td headers="p_value" class="gt_row gt_right">5.79 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.555</td>
## <td headers="entrez_id" class="gt_row gt_right">101882702</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101882847</td>
## <td headers="gene_description" class="gt_row gt_left">rho-associated protein kinase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.901</td>
## <td headers="p_value" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">1.69 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.901</td>
## <td headers="entrez_id" class="gt_row gt_right">101882847</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883008</td>
## <td headers="gene_description" class="gt_row gt_left">zinc finger protein 664-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.424</td>
## <td headers="p_value" class="gt_row gt_right">4.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.68 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.424</td>
## <td headers="entrez_id" class="gt_row gt_right">101883008</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883092</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.709</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.709</td>
## <td headers="entrez_id" class="gt_row gt_right">101883092</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC101883092</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.508</td>
## <td headers="p_value" class="gt_row gt_right">2.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.508</td>
## <td headers="entrez_id" class="gt_row gt_right">101883092</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC103910189</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pirellula</td>
## <td headers="correlation" class="gt_row gt_right">−0.681</td>
## <td headers="p_value" class="gt_row gt_right">2.40 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.681</td>
## <td headers="entrez_id" class="gt_row gt_right">103910189</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC103910189</td>
## <td headers="gene_description" class="gt_row gt_left">interferon-induced very large GTPase 1-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Runella</td>
## <td headers="correlation" class="gt_row gt_right">−0.471</td>
## <td headers="p_value" class="gt_row gt_right">2.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">5.76 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.471</td>
## <td headers="entrez_id" class="gt_row gt_right">103910189</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC108183350</td>
## <td headers="gene_description" class="gt_row gt_left">protein NLRC3-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Luteolibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.369</td>
## <td headers="p_value" class="gt_row gt_right">3.50 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.03 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.369</td>
## <td headers="entrez_id" class="gt_row gt_right">108183350</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC110438438</td>
## <td headers="gene_description" class="gt_row gt_left">serine/threonine-protein kinase 38-like</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.539</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">2.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.539</td>
## <td headers="entrez_id" class="gt_row gt_right">110438438</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC110439307</td>
## <td headers="gene_description" class="gt_row gt_left">uncharacterized LOC110439307</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">−0.661</td>
## <td headers="p_value" class="gt_row gt_right">8.19 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">9.70 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.661</td>
## <td headers="entrez_id" class="gt_row gt_right">110439307</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487211</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487211</td>
## <td headers="taxa_id" class="gt_row gt_left">Culicoidibacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.378</td>
## <td headers="p_value" class="gt_row gt_right">5.89 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">7.37 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.378</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137487211</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137487211</td>
## <td headers="taxa_id" class="gt_row gt_left">Flavobacterium</td>
## <td headers="correlation" class="gt_row gt_right">−0.731</td>
## <td headers="p_value" class="gt_row gt_right">3.90 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.731</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137489722</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137489722</td>
## <td headers="taxa_id" class="gt_row gt_left">Pseudoxanthomonas</td>
## <td headers="correlation" class="gt_row gt_right">0.979</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">6.20 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.979</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137496053</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137496053</td>
## <td headers="taxa_id" class="gt_row gt_left">Gemmobacter</td>
## <td headers="correlation" class="gt_row gt_right">−0.463</td>
## <td headers="p_value" class="gt_row gt_right">1.10 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.34 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.463</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## <tr><td headers="analysis" class="gt_row gt_left">Historical_Contingency_Recovery</td>
## <td headers="gene_id" class="gt_row gt_left">LOC137496184</td>
## <td headers="gene_description" class="gt_row gt_left">LOC137496184</td>
## <td headers="taxa_id" class="gt_row gt_left">Bosea</td>
## <td headers="correlation" class="gt_row gt_right">−0.522</td>
## <td headers="p_value" class="gt_row gt_right">4.00 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="fdr" class="gt_row gt_right">4.15 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="abs_correlation" class="gt_row gt_right">0.522</td>
## <td headers="entrez_id" class="gt_row gt_right">NA</td>
## <td headers="go_terms" class="gt_row gt_left">NA</td>
## <td headers="kegg_pathways" class="gt_row gt_left">NA</td></tr>
## </tbody>
##
##
## </table>
## </div>
##
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## <td colspan="6" class="gt_heading gt_title gt_font_normal" style>KEGG Pathway Enrichment Results</td>
## </tr>
## <tr class="gt_heading">
## <td colspan="6" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Recovery</td>
## </tr>
## <tr class="gt_col_headings">
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="KEGG ID">KEGG ID</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_left" rowspan="1" colspan="1" scope="col" id="Pathway Description">Pathway Description</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Adjusted P-value">Adjusted P-value</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene Count">Gene Count</th>
## <th class="gt_col_heading gt_columns_bottom_border gt_right" rowspan="1" colspan="1" scope="col" id="Gene IDs">Gene IDs</th>
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## <tbody class="gt_table_body">
## <tr><td headers="ID" class="gt_row gt_left">dre04622</td>
## <td headers="Description" class="gt_row gt_left">RIG-I-like receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">3.11 × 10<sup style='font-size: 65%;'>−3</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">100148871/405785/564854</td>
## <td headers="log_padj" class="gt_row gt_right">2.507</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04621</td>
## <td headers="Description" class="gt_row gt_left">NOD-like receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">2.31 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">405785/564854/641582</td>
## <td headers="log_padj" class="gt_row gt_right">1.637</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre05132</td>
## <td headers="Description" class="gt_row gt_left">Salmonella infection</td>
## <td headers="p.adjust" class="gt_row gt_right">7.93 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">3</td>
## <td headers="geneID" class="gt_row gt_right">100006857/405785/448942</td>
## <td headers="log_padj" class="gt_row gt_right">1.101</td></tr>
## <tr><td headers="ID" class="gt_row gt_left">dre04620</td>
## <td headers="Description" class="gt_row gt_left">Toll-like receptor signaling pathway</td>
## <td headers="p.adjust" class="gt_row gt_right">7.93 × 10<sup style='font-size: 65%;'>−2</sup></td>
## <td headers="Count" class="gt_row gt_right">2</td>
## <td headers="geneID" class="gt_row gt_right">405785/564854</td>
## <td headers="log_padj" class="gt_row gt_right">1.101</td></tr>
## </tbody>
##
##
## </table>
## </div>
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## <td colspan="2" class="gt_heading gt_subtitle gt_font_normal gt_bottom_border" style>Historical Contingency of Recovery</td>
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## <tr><td headers="Metric" class="gt_row gt_left">Total significant gene-taxa correlations</td>
## <td headers="Value" class="gt_row gt_right">97</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique genes involved</td>
## <td headers="Value" class="gt_row gt_right">63</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Unique taxa involved</td>
## <td headers="Value" class="gt_row gt_right">24</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched GO terms</td>
## <td headers="Value" class="gt_row gt_right">0</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Enriched KEGG pathways</td>
## <td headers="Value" class="gt_row gt_right">4</td></tr>
## <tr><td headers="Metric" class="gt_row gt_left">Mean absolute correlation</td>
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## <tr><td headers="Metric" class="gt_row gt_left">Median absolute correlation</td>
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##
## === INTEGRATED ANALYSIS SUMMARY ===
| Integrated Analysis Summary | ||||
| Gene-taxa correlations across three key research questions | ||||
| Research Question | N Significant Correlations | N Significant Partial Correlations | N Unique Genes | N Unique Taxa |
|---|---|---|---|---|
| 1. Parasite Exposure Response (A- T- P- vs A- T- P+) | 11681 | 80 | 2368 | 22 |
| 2. Historical Contingency of Parasite Response (A- T- P+ vs A+ T- P+ vs A- T+ P+ vs A+ T+ P+) | 4 | 2 | 3 | 3 |
| 3. Historical Contingency of Recovery (A- T- P- vs A+ T- P- vs A- T+ P- vs A+ T+ P-) | 144 | 97 | 78 | 30 |
##
## === FILES SUMMARY ===
## Results directory: /Users/michaelsieler/Dropbox/Mac (2)/Documents/Sharpton_Lab/Projects_Repository/Rules_of_Life/major-experiment-2023/Code/Analysis/DEGxDAT/Results
## Configuration: LOAD_EXISTING_RESULTS = TRUE
## Analysis results were loaded from existing files.
## To recompute results, set LOAD_EXISTING_RESULTS = FALSE in the configuration section.
##
## Available result files:
## - all_enrichment_results_20250707_115941.rds (modified: 2025-07-07 11:59:54 )
## - all_enrichment_results_20250707_120317.rds (modified: 2025-07-07 12:03:18 )
## - results_all_20250630_200742.rds (modified: 2025-06-30 20:08:10 )
## - results_historical_contingency_20250630_200918.rds (modified: 2025-06-30 20:09:18 )
## - results_parasite_20250630_200828.rds (modified: 2025-06-30 20:08:28 )
## - results_recovery_20250630_201002.rds (modified: 2025-06-30 20:10:02 )
```